Andrea Jonas, MD, Rebeca A. Aslakson, MD, PhD, Meghan Ramsey, MD, Kristan Staudenmeyer, MD, Arthur Sung,  MD, Jenny Wilson, MD MSc, Angela Rogers, MD, MPH, Stanford University 

Contact: Andrea Jonas andreajonas@stanford.edu

Creating a Critical Care Task Force: Meeting an Urgent Need

In early March 2020, the growing number of COVID-19 cases in Santa Clara County in California suggested the potential for a major surge in intensive care unit (ICU) patient volume. In response Stanford Hospital took urgent action: training staff, ordering supplies, and developing Stanford’s COVID-19 response team. We describe here the process pathways by which Stanford Hospital implemented a COVID-19 Critical Care Task Force (CCTF) to provide real-time institutional and regional guidance on patient care and surge planning during the COVID-19 pandemic.

Key to the success of Stanford’s CCTF was leveraging the in-house expertise cultivated over decades, including clinician leaders, data scientists, and a broad, multidisciplinary team of hospital employees committed to delivering excellent patient care in the face of extraordinary circumstances. While timely county and state-level shelter-in-place orders mitigated the patient surge seen at Stanford, the lessons garnered in readiness and communication across multiple disciplines are broadly applicable.

Laying The Foundation Of A Covid-19 Critical Care Task Force

The formation of the CCTF stemmed from two elements core to Stanford’s hospital ethos: a culture of multidisciplinary collaboration coupled with a clear central organizing structure. Stanford’s hospital leadership formed the Clinical Oversight Resource Team (CORT) committee to serve as a single entity to oversee the COVID-19 response. The CORT committee included key stakeholders from Stanford’s physician and nursing leadership, and ensured coordination of the many elements of COVID-19 preparations, including laboratory testing, occupational health, supply chain, workforce management, and patient care practices and protocols.

Central to CORT’s mission was enabling individual hospital departments to develop task forces that would each lead their respective department through the rapidly evolving clinical landscape. One such committee was the CCTF, developed under CORT’s guidance, and empowered to serve as a cornerstone group in leading Stanford’s ICU response (Figure 1).

Figure 1: Organizational chart revealing tasks key at each level of the hospital leadership structure to achieve timely dissemination of protocols and practices for the hospital’s COVID-19 response

Stanford’s CCTF provided clinical guidance to the institution’s provision of critical care, issuing consensus-based recommendations from a collaborative, multidisciplinary team. Ultimately growing to include over sixty members, the CCTF included representatives from over a dozen departments, including intensivists from multiple specialties, nursing and respiratory therapy, pharmacy, ethics, and infectious disease at Stanford Hospital (Table 1).

Table 1 Breakdown of multidisciplinary representation by department on Stanford’s CCTF.

Task force members met virtually up to three times weekly to review the COVID-19 literature, clinical trials, and professional society clinical guidelines to generate expert consensus recommendations. The task force also solicited informal sources of data, including expert consensus on best clinical practices from peer institutions and guidance from colleagues at epicenters hit early in the pandemic (including China, Italy, and New York).

Through these collaborative efforts, the task force developed and implemented surge planning for the ICU, produced over two dozen protocols and checklists for the care of COVID-19 patients, and developed and disseminated web-based COVID-19 educational resources for the entire hospital community. These resources are continually reviewed and updated to reflect rapidly emerging evidence on critical illness due to COVID-19.

Innovations in Data Science for Surge Planning

Real-time data analytics and a close relationship with the data science team proved crucial for ICU surge planning by the CCTF. Twice weekly, case counts and model predictions were reviewed with the task force to marry computer modeling of local case prevalence data with the observed number of cases seen in the Stanford hospital network. Contributing key data such as ICU length of stay, CCTF members facilitated high fidelity modeling of expected hospital and ICU census for future weeks.

This modeling data allowed Stanford to anticipate several weeks in advance when ICU capacity would be exceeded and to develop appropriate surge response strategies. As the beneficial impact of local shelter-in-place orders became apparent, Stanford’s data modeling predicted a steady decrease in new ICU admissions, allowing deactivation of the surge team. Data modeling continues in real-time as of June 2020, incorporating Stanford’s hospital census, aggregate laboratory testing results, and public health data of the surrounding counties. In this capacity, the CCTF continues to collaborate with data scientists to anticipate potential future surges in regional COVID-19 prevalence.

Scheduling of surge team providers required communication across multiple disciplines. A core leadership team consisting of intensivists, nurses, and respiratory therapists mapped out the labor needs for multiple surge scenarios. Anticipating that patient volume could rapidly outstrip labor pool capacity, alternative staffing models (such as the Society of Critical Care Medicine’s pandemic response strategy) were developed and criteria for activating such strategies were agreed upon. Throughout the process, providers had access to the surge schedule, and projected dates for surge activation, allowing individuals to know whether they were “on deck” for COVID-19 surge-related clinical duty. Though Stanford’s patient surge was not dramatic, the multidisciplinary approach allowed Stanford to seamlessly activate its ICU surge teams once capacity was reached.

Clinical Care: Generating and refining protocols in a rapidly changing landscape

A core CCTF responsibility was the development of clinical pathways for evidence-based practice to promote a consistent approach throughout the hospital. Intense scientific and public interest in COVID-19 led to exponential growth in observational studies and clinical trials focused on COVID-19 patients. Rapid roll-out of numerous clinical trials was mirrored by the publication of clinical guidelines and expert consensus statements which were sometimes contradictory and often rapidly outdated. In the face of a rapidly evolving clinical landscape, the CCTF was tasked with charting a course for the provision of up-to-date, evidence-based best practices for COVID-19 patients in Stanford’s ICUs.

Leveraging the multi-disciplinary expertise brought by the task force members, the CCTF generated protocols to guide all aspects of ICU care at Stanford Hospital. Topics included code team staffing, personal protective equipment (PPE) requirements for ICU procedures, intubation and ventilator strategies for COVID-19 patients, and criteria for use of interventions like non-invasive positive pressure ventilation, proning, and extracorporeal membrane oxygenation. Each protocol was drafted by a multi-disciplinary subcommittee, composed of 2-5 task force members.

Subcommittees completed relevant review of existing literature, professional guideline recommendations, and surveyed practices at peer institutions. Ultimately, each subcommittee generated a 1-2 page document summarizing protocol recommendations along with the rationale and supporting literature. Completed protocols were discussed with the entire CCTF membership, allowing time for input and recommendations from the wider CCTF audience, before ultimately being sent to the CORT committee for final approval and dissemination (Figure 2).

Figure 2: Overview of protocol development, from need identification through iterative review process to approval and dissemination.*

*Source: Icons made by Flat Icons from www.flaticon.com.

Through this process, the CCTF generated over two dozen protocols pertaining to the care of the critically ill COVID-19 patient. The task force continues to evaluate emerging data regarding optimal COVID-19 critical care practices and continually modifies and updates available protocols and educational resources. This is accomplished through delegation to individual subcommittees, which ensures continued updating as needed of protocols, with revisions discussed by the entire CCTF and ultimately approved by CORT.

Disseminating Information: Widespread sharing for point of care access

Rapid protocol development by the CCTF required equally rapid dissemination of knowledge to front-line providers. CCTF resources were shared broadly within the institution: CCTF protocols were announced in multiple departmental and intra-institutional newsletters and emails; posted on the hospital intranet; shared as a resource in diverse institutional training programs; referenced during weekly Department of Medicine grand rounds (which also frequently included presentations by CCTF leadership); and broadcast on institutional social media platforms.

To meet the needs of front-line providers, the CCTF recognized the need to disseminate resources in an easy-to-use, widely-accessible format. Recommendations and guidelines were posted to a centralized website, which is both searchable and externally available to support front-line providers in real-time (Figure 3). Examples of available resources include educational videos on PPE donning/doffing, infographics on modalities for oxygen support for COVID-19 patients, a “survival guide” for non-intensivist physicians working in the ICU environment, and step-by-step guides for patient proning. The website is formatted to be accessible on mobile devices, with the intention of serving as an “in the pocket” summary of task force recommendations for point of care use.

Website link: https://sites.google.com/view/stanfordcovid/home

Figure 3: Sample protocol on non-invasive positive pressure ventilation for front-line use.

Implementing real-time systems-level change

Rapid-cycle communication with Stanford leadership allowed the CCTF to implement real-time systems-level changes in patient care. Beyond protocol implementation impacting front-line care delivery, the CCTF identified and addressed systems-level issues pertaining to the delivery of care. For example, as the census of COVID-19 positive ICU patients grew, so did calls to cohort all COVID-19 patients into a single ICU ward to conserve PPE and limit potential contamination within the hospital building. The clear path between CCTF and hospital leadership through the CORT committee enabled rapid establishment of a cohorting structure. Further examples of rapid-cycle systems-level changes included the restructuring of labor pools to meet the needs for ICU surge planning, implementation of a COVID-19 airway team, roll out of a virtual patient video monitoring system in the ICU, and approval for equipment ordering to meet the unique ICU needs of COVID-19 patients.

Measuring the Impact of the Critical Care Task Force

Several measures demonstrate the impact of the CCTF’s recommendations and resources. At peak utilization, the CCTF website had over 80 daily unique visitors, and has amassed over one thousand page views. The CCTF YouTube video demonstrating proper donning and doffing of PPE has garnered over 800 views. The impact of the task force has been felt regionally, as Bay Area community hospitals have reported referencing CCTF guidelines in developing their own protocols for care of COVID-19 patients.

Bringing The Lessons Forward: Continued Growth

A multidisciplinary, collaborative approach was required to meet the rapidly developing challenges posed by COVID-19. Frequent communication across multidisciplinary teams served as the foundation of Stanford’s COVID-19 response, and an iterative, collaborative process allowed rapid progress. The success of the CCTF was built on the foundation of institutional support and a culture of collaborative, multidisciplinary care, and provides one model for hospital preparations for a surge in critically ill patients.

Glossary of Terms

CCTF           Critical Care Task Force

CORT           Clinical Oversight Resource Team

ICU              Intensive Care Unit

PPE             Personal Protective Equipment

References

 https://www.medrxiv.org/content/10.1101/2020.03.26.20044842v3

https://www.medrxiv.org/content/10.1101/2020.03.24.20042762v1

Aaron Baird and William Olivera, Georgia State University

Health Information is Valuable but Difficult to Share

The U.S. Centers for Disease Control (CDC) is in the early stages of offering a COVID-19 electronic case reporting application. This application is meant to help health care providers more easily connect to public health agencies and submit reports of potential COVID-19 cases.[1] But, with all of the investment into electronic health records (EHRs) by health care providers, why is this electronic case reporting application even needed in the first place?

The reason is that health information is not shared as readily as it should be in the U.S. due to a variety of technical, competitive, and political issues. Health care is fundamentally a market for information, and information is excludable.^1,2 In particular, rights to information are often retained, or at least managed, by the entity collecting and storing the information. In the case of health care, patient health information is typically collected and stored by health care providers and associated ancillaries such as laboratories, imaging centers, and pharmacies.³ While patients may retain the right to access this information, as afforded by the Health Insurance Portability and Accountability Act,⁴ health information often is difficult to share.

For instance, many cases of information blocking have led the U.S. to pass additional provisions focused on enhancing health information interoperability and penalizing purposeful blocking of authorized exchange.^5,6 Further, it has been reported that connecting to public health agencies can be especially challenging during this pandemic due to barriers such as lack of interfaces available from such agencies.⁷ What is particularly troubling, especially during a pandemic, is that barriers to health information sharing can delay possible treatments, lengthen economically draining quarantines, and cost lives.

An ideal scenario, as described in other opinion papers,⁸ would be a national health IT infrastructure that houses accurate and reliable public and population health data. This type of approach could give the U.S. a readily available source to track and trace individuals showing COVID-19 symptoms or testing positive.^9,10 However, the lack of a unique national patient identifier and the reluctance of health care organizations to participate, as well as political and technical challenges, are immediate barriers for adoption for such infrastructure.¹¹

Limits to Current Information Exchange Standards

To overcome such barriers, health care stakeholders have been working to develop a modern health information exchange standard. The result of these efforts is a standard referred to as Fast Healthcare Interoperability Resources (FHIR). While considered with skepticism early on, especially due to the relative recency of the HL7 v3 debacle,¹² FHIR has rapidly evolved and become one of health care’s best hopes for addressing many of the challenges associated with sharing health information. In fact, the positive momentum has resulted in the U.S. Office of the National Coordinator of Health Information Technology mandating the adoption of FHIR HL7 v4 by spring of 2022 for all health care providers.¹³ Many health IT vendors are now building FHIR support into their products, and many health organizations are beginning to use FHIR to exchange information and support health applications.^14-16

However, not all EHR and health information systems enable FHIR-based APIs in their products and not all health information exchange use cases have been fully accounted for. For instance, FHIR-based electronic case reporting will reportedly be enabled by the CDC’s application we mentioned earlier. Reporting COVID-19 cases via such FHIR-based interfaces requires that the health care provider already have FHIR-based APIs available and enabled within their EHR. It also requires that rules be established by the health care provider for which data should trigger case reports and which data should be collected and transferred when seeking to report positive or suspected COVID-19 cases. Further, even with the availability of the COVID-19 electronic case reporting application from the CDC, it is still thought that electronic case reporting and contact tracing will remain highly variable.^17-19 For instance, underprivileged areas with limited technological resources may need to rely on other forms of reporting.[2]

Thus, health information sharing is not frictionless. Pandemics can further exacerbate such frictions, as even less resources are typically available for improving or even improvising required rules, agreements, and interfacing. As has been well documented over the past several years, digitizing health information is only part of the challenge.²⁰ The subsequent and perhaps more consequential challenge is using digital health information, often obtained and aggregated from multiple health care providers, to improve overall health and health care.^21-25

Given these current limits, the goal of this paper to critically assess health information sharing, particularly from the perspective of reducing exclusion during a pandemic.

In the next section, we provide more detail about how club theory applies to health information sharing. In the following sections, we propose what we term transitional mechanisms that can be applied by academics and practitioners when considering how to best transition health information from a club good to more of a public good during a pandemic, and potentially back to a club good post-pandemic. We also consider how these transitional mechanisms can be applied in practice, especially during the following stages of a pandemic: 1) early detection and investigation, 2) comprehensive assessment, and 3) monitoring.²⁶

Insights from Club Theory

Health information is a club good

We consider health information to be a club good or quasi-public good in that it is both non-rivalrous and excludable. That is, consumption by one entity does not prevent consumption by another entity, yet sharing can be prevented, and benefits can be monopolized or restricted to members of a club.^3,27-29

Club theory considers goods that are not entirely private or public. It often focuses on explaining how membership costs of joining clubs, potential congestion within clubs, rivalries between clubs, and externalities generated by clubs impact resource use and outcomes.^28,30,31 Members join the club to reduce production costs or impose exclusion on a good, or both, but also must consider congestion as too many members may result in negative externalities.^28,30,31 Thus, membership in clubs is often limited to the number of people for which benefits can be generated without excessive congestion.

Rival health information clubs have long faced barriers to exchange

Due to such membership limitations, clubs can be rivals in that the capabilities of each club vary, and competition for network effects creates competition between clubs. Further, given that the capabilities of each club vary, attracting members requires differentiated value propositions,³² meaning that health information market partitioning is a regular practice by such clubs. For example, club members can exchange health information using automated FHIR-based interfaces (i.e., APIs for structured data) or secure email exchange of records as PDFs (i.e., unstructured data that is difficult to query or aggregate) depending on their technological capabilities.³³

These health information sharing clubs can include state-level health information exchanges; public health information interfaces, including CDC’s new case reporting app for COVID-19; and even exclusive partnerships with private companies such as Apple, Google, or Epic that share information between participants. However, sharing between such clubs requires deliberate efforts.

While it might be easy to argue that membership costs for health information sharing are gradually being reduced and heading toward much lower marginal costs, many years of work in improving health information sharing capabilities have not reduced the marginal costs to anywhere near zero. While these costs might be much lower in the future, currently the cost of interfacing remains a barrier. Further, membership costs may also include fees that at least cover the overhead of the club.

Membership costs, even if declining, are unavoidable, and market partitioning happens regularly. Further, health information has never been entirely treated as public good in order to protect patient privacy. Simply releasing all information into the public domain is also not an option.

We expect membership costs for joining health information sharing clubs to be present into the foreseeable future. This means that the excludability characteristic of health information is not simply going to disappear over time.

The essential question then becomes, what can be done prior to and during a pandemic to reduce health information excludability, thereby enhancing the potential for health information sharing?

Current suggestions for information exchange during a pandemic are inadequate

The most obvious answer is to make accommodations or even improvisations that relax health information exclusions and shift health information more toward a public good, especially during a pandemic. While the privacy of identifiable patient information prevents full public disclosure, health information rights can be customized to meet public health needs. For instance, one might send only COVID-19 case reports to public health agencies rather than records for all patients irrespective of diagnosis or symptoms.

While making such accommodations and even improvisations makes intuitive sense, in practice there are formidable challenges associated with fragmented health information and varying needs for accessing this information. As an example of this challenge, consider how a health care provider should determine whether or not to flag a case as a potential COVID-19 case.^34,35 Should the record be flagged as reportable only if a positive test is obtained? If testing is not available, is inconclusive, or is delayed, are there particular symptoms, lab test values, or observables that should then be leveraged to make a potentially positive or negative case decision? Further, which entities should be making the final decisions as to what benchmarks that subjective or ambiguous data should reach prior to arriving at the judgment for a case?

As a consequence of such decision making, one solution would simply be to send all even suspected cases to local public health agencies. Unless such agencies have substantial resources, however, too much information on too many patients will not be helpful and may overwhelm available resources.

As opposed to over-reporting, under-reporting is also a significant risk, as too little information may exacerbate the spread or delay potential investigations required to facilitate contact tracing. Further, fragmented information, especially if a patient has visited multiple providers or has provided incomplete information such as incomplete travel history may complicate the efforts that need to be taken by agencies receiving such information.

In sum, while we propose that health information should be less excludable during a pandemic, club theory currently does not provide an answer into how to make exclusion transitions during and even after pandemics. Concepts such as membership costs, rivalries, and externalities provide an excellent framing,^28,30,31 and we have seen models that attempt to capture the complexity of infectious diseases using a loose coupling framework.³⁶ What is missing, though, are the mechanisms by which health information excludability could be reduced in a crisis.

Transitional Mechanisms: Rights Flexibility and Club Coordination

To address this theoretical and practical challenge, we propose two mechanisms related to supporting and enabling reductions in health information exclusion during pandemics: 1) rights flexibility, and 2) club coordination. We view these mechanisms as transitional in that they can help to shift from health information exclusiveness in a pre-pandemic time period to more sharing during a pandemic response. Further, they can also aid in returning exclusion restrictions to pre-pandemic levels once a pandemic has passed or stabilized.

Health information rights flexibility

Health information rights are the ownership of or entitlement to health information.^3,4,37 Health information rights flexibility would be the ability to flexibly change ownership rules and entitlement requirements associated with health information during a time of need or crisis. Exclusive rights to health information lie on one end of the continuum of private and club goods, while non-exclusive rights to public goods lie on the other end of the continuum.

Information ownership during normal times: Pre-pandemic, rights tend to be exclusive. In fact, it would not even be known in a pre-pandemic time period which health information other entities would need rights to, as the nature of a future virus would unknown.

Flexibility during a pandemic: During a pandemic, more information becomes available about the virus, associated symptoms and markers or relevant diagnostics, and potential treatments. In turn, relevant categories of health information can be identified as needed by public health agencies and organizations doing disease and treatment research and development. These categories of information can thus be classified as requiring less exclusions. Rights can then be distributed to the entities requiring access.

EHR design: It is easy enough for a government or regulatory agency to temporarily change rights to health information, such as by saying that all COVID-19 cases must be reported to public health agencies,[3] thereby loosening the exclusive hold on such data by health care providers. However, it is much more difficult to quickly change health information systems, such as EHRs, to abide by such new regulations. Thus, we propose that rights flexibility be designed directly into health information systems, such as EHRs and data warehouses maintained by health care providers and health information sharing clubs.

Limits to date: Some efforts have been made in this regard by public health agencies, such as with the Reportable Conditions Knowledge Management System being rolled out by the CDC as well as use of the National Health Safety Network for consolidating case reporting.[4] However, real-time or near real-time linking and coordinating of dynamic health information access rules within and between health care providers, public health agencies, and other stakeholders has not been a priority.

Opportunities for rules engines: We propose that health information systems be modified to incorporate rules engines that define health information rights with respect to conditions that may require more access, such as COVID-19. Such rules engines should support rapid modification of health information rights at a granular level, with detail at least per condition, and potentially by groups of symptoms and diagnostic values. The rules engines also require dynamic updating of interfaces, such as with public health agencies and other relevant health information clubs, that need access to such information.

Health information club coordination

We also propose club coordination as an essential mechanism for transitioning toward less health information exclusion during a pandemic. Coordination traditionally focuses on two activities: “managing dependencies between activities;” ³⁸ and “the process of interaction that integrates a collective set of independent tasks,” including conditions of accountability, predictability, and common understanding.³⁹

Coordination between clubs: Because coordination typically occurs within health information clubs, as this is the purpose of the club, the primary challenge during a pandemic is coordinating between clubs. A key challenge is that health information is not homogenous between clubs, as information is shared in more or less granularity depending on the nature of the club or may even be transformed in different ways depending on the nature of the club.

For instance, claims data payment information may be shared with insurance companies and payers such as the Centers for Medicare and Medicaid Services, but such claims data does not include granular clinical data required for deeply investigating a case. Nonetheless, such claims data does provide information about diagnoses and procedures and could be useful in assessing treatment variation for a specific diagnosis. Given the heterogeneous nature of health information, as well as potential rivalries between often competing clubs and coordination of data often required when assessing causes and effects in pandemics, we propose club coordination as an essential pandemic mechanism.

Data dictionaries: In particular, we propose that health information clubs be required to maintain data dictionaries in pre-pandemic time periods, or rapidly develop such data dictionaries during a pandemic. These data dictionaries can then be accessed by public health agencies and other relevant stakeholders, without compromising security or privacy, when seeking to determine which health information fields, formats, and transformations are immediately available for data collection and analysis.

We further propose that during a pandemic, health information clubs be required to identify areas of overlap or uniqueness between their data dictionary and data dictionaries of other health information clubs that report to the same public health agencies or stakeholders. For instance, health information club A might have patient travel data while health information club B does not have this data but has other relevant clinical data such as essential lab test results. In such a case, knowledge of which data is unique and which is common to other clubs will help relevant agencies quickly identify which health information to request from each club.

In this way, interdependencies between clubs can be identified rapidly. Knowledge of the linkages can help reduce coordination and congestion costs for public health agencies and other stakeholders in need of the information.

Aligning the Transitional Mechanisms with Pandemic Stages

 We now briefly consider how to align these two mechanisms with the three stages of pandemic surveillance proposed by the World Health Organization: (1) early detection and investigation, (2) comprehensive assessment, and (3) monitoring (Table 1).²⁶

Pandemic surveillance stage 1: Early detection and investigation

During the first stages of pandemic surveillance, involving early detection and investigation, the goal is to detect human-to-human transmission, characterize the features of the new disease, and define high-risk groups to prioritize interventions.²⁶ Time is critical during these initial stages, particularly when assessing the magnitude of the new disease.

From a rights flexibility perspective, we propose that during this initial stage, health information systems need to have rules engines that can quickly adjust information rights corresponding to the health information needed to assess disease magnitude and infection rates. In cases where no standard interface with one or more public health agencies has yet to be established, health information sharing clubs would be asked to manually share required health information to enable rapid response and avoid evaluation delays.^40-42 This would be an incentive to rapidly invest in interfaces or to have such interfaces developed in advance of a pandemic.

An issue, however, may be that public health agencies may lack the proper technology infrastructure to receive and evaluate electronic case reports, especially at high volumes.⁷ Therefore, during this initial stage, public health agencies should evaluate their capacity and bandwidth to receive electronic case reports. If insufficient capacity is available, electronic case reports should be set to be conservatively triggered, such as only when a positive test is received for a case. Such an approach will help avoid confestion and cognitive overload issues at the public health agency. If on the other hand sufficient capacity is available, triggers on electronic case reports can be set to report more liberally, such as not only when positive tests occur but also when symptoms are consistent with having the disease.

From a club coordination perspective, while we have proposed that health information clubs maintain data dictionaries in pre-pandemic periods, we also suggest clubs communicate with each other during this stage, to determine health information overlaps and gaps between them. The goal of this exercise is for health information clubs to take an active role in determining which clubs will be the most reliable combined sources of information for public health agencies and other relevant stakeholders.

Pandemic surveillance stage 2: Comprehensive assessment

During the comprehensive assessment stage of a pandemic, the goal is to facilitate more effective responses at both national and international levels. Public health agencies will need to characterize the epidemiological features of the outbreak, such as the distribution of cases and deaths by age group, describe the impact of the illness on the community, and define transmission characteristics, such as incubation period and epidemiological curve.²⁶

From a rights flexibility perspective, we recommend evaluating the rule engines based on the early assessment from authoritative sources, such as the CDC. At this point, public health agencies should be able to determine if they are missing health data or if there is an inconsistent overlap of data. Rule engines will need to be correspondingly updated as knowledge about disease transmission and progression evolves.

At the same time, public health agencies can also look at the patient population not included in the electronic case reports, which may not be included in the tracked by interfaced health information clubs. This information should be shared with relevant health information clubs that should subsequently determine what health facilities, stakeholder, or technology applications are in the best position to collect the needed information on the missing population.

From a club coordination perspective, in this stage health information clubs should evaluate opportunities to improve data sharing. They should evaluate what other sources of data could be explored or may be beneficial to share with public health agencies. At the same time, EHR or health applications that are sources of essential information should be updated based on the latest field recommendations from public health authorities. For example, during COVID-19, the CDC released new data entry codes (e.g., ICD-10, CPT, and LOINC) to capture pandemic related data. The new codes, as well as standardizing naming conventions across applications within clubs, can help facilitate improve needed health information collection, aggregation, and analysis.¹⁷

Pandemic surveillance stage 3: Monitoring

As the pandemic moves to the monitoring stage, public health agency information needs may be more standardized rather than exploratory, as more is now known about what should trigger an electronic case report and what symptoms should be tracked over time.⁴³ Once public health agencies have a better understanding of the disease, they are likely to suggest additional or more standardized guidelines that help transition pandemic surveillance to monitoring. Under monitoring, public health agencies track the disease in terms of geographical spread, intensity, and impact. It is also essential to highlight and look for cases that fall outside of typical, known patterns. At this stage, health information clubs should prioritize the accuracy and reliability of information over volume.

In terms of rights flexibility, health systems should implement procedures and processes to reconcile the number of case reports occurring within the health care system in relation to the number of cases being reported to public health agencies. The procedures should provide direction to improve patient matching and leverage additional fields such as demographic data and, if available, relevant social and genetic data. At the same time, health care organizations should create guidelines to address the patient privacy of newly required fields as well as how to handle ongoing data collection as the pandemic moves to a more stable period, such as by creating sunset clauses on data collection trace applications.⁴⁴

From a club coordination perspective, electronic case reporting reconciliation should occur between individual health systems and the health information club they are a part of. Health information clubs can also discuss with their members the efficiency of the data collected and determine additional best process to efficiently capture, store, and share reliable data. For example, they can determine the best health entity, stakeholder, or technological application to capture reliable and accurate data. At the same time, they can determine fields that are not useful to capture or are redundant between the health information clubs. Finally, after a systematic assessment of fields, they should update data dictionaries within the clubs.

Looking Forward

We have identified that sharing health information is essential to pandemic responsiveness. Using ideas from club theory, we have analyzed the challenges associated with information exclusion by health information sharing clubs during a pandemic. We propose that reducing health information rights exclusion is necessary during a pandemic while highlighting current difficulties in achieving this goal.

We propose two mechanisms that can achieve greater information sharing during a pandemic: rights flexibility and club coordination. When paired with specific modifications to health information systems as recommended in this paper, including incorporating rights engines and and keep up-to-date data dictionaries, these mechanisms can accelerate health information exchange pre-, during, and post-pandemic.

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16. Khalilia M, Choi M, Henderson A, Iyengar S, Braunstein M, Sun J. Clinical predictive modeling development and deployment through FHIR web services. Paper presented at: AMIA Annual Symposium Proceedings2015; San Francisco.
17. Miri A, O’Neill DP. Accelerating Data Infrastructure For COVID-19 Surveillance And Management. Health Affairs Blog 2020; https://www.healthaffairs.org/do/10.1377/hblog20200413.644614/full/. Accessed April 26, 2020, 2020.
18. Vest JR, Gamm LD. Health information exchange: persistent challenges and new strategies. Journal of the American Medical Informatics Association. 2010;17(3):288-294.
19. Vest JR, Menachemi N. A population ecology perspective on the functioning and future of health information organizations. Health Care Management Review. 2019;44(4):344-355.
20. Kellermann AL, Jones SS. What It Will Take To Achieve The As-Yet-Unfulfilled Promises Of Health Information Technology. Health Affairs. 2013;32(1):63-68.
21. Furukawa MF, King J, Patel V, Hsiao C-J, Adler-Milstein J, Jha AK. Despite substantial progress in EHR adoption, health information exchange and patient engagement remain low in office settings. Health Affairs. 2014;33(9):1672-1679.
22. Ayabakan S, Bardhan I, Zheng ZQ, Kirksey K. The Impact of Health Information Sharing on Duplicate Testing. MIS Quarterly. 2017;41(4):1083-1103.
23. Miller AR, Tucker C. Health information exchange, system size and information silos. Journal of Health Economics. 2014;33:28-42.
24. Rudin RS, Motala A, Goldzweig CL, Shekelle PG. Usage and effect of health information exchange: a systematic review. Annals of internal medicine. 2014;161(11):803-811.
25. Rahurkar S, Vest JR, Menachemi N. Despite the spread of health information exchange, there is little evidence of its impact on cost, use, and quality of care. Health affairs. 2015;34(3):477-483.
26. WHO. Global Surveillance during Influenza Pandemic. 2009; Global Influenza Programme. Available at: https://www.who.int/csr/disease/swineflu/global_pandemic_influenza_surveilance_apr09.pdf. Accessed 04/29/2020, 2020.
27. Rodwin MA. The case for public ownership of patient data. JAMA. 2009;302(1):86-88.
28. Chohan UW, D’Souza A. Club Theory: A Contemporary Economic Review. Available at SSRN 3512557. 2020.
29. Wooders M. The theory of clubs and competitive coalitions. Annu Rev Econ. 2012;4(1):595-626.
30. Buchanan JM. An economic theory of clubs. Economica. 1965;32(125):1-14.
31. Sandler T, Tschirhart J. Club theory: Thirty years later. Public Choice. 1997;93(3-4):335-355.
32. Vest JR, Ancker JS. Health information exchange in the wild: the association between organizational capability and perceived utility of clinical event notifications in ambulatory and community care. Journal of the American Medical Informatics Association. 2017;24(1):39-46.
33. Braunstein ML. Health Informatics on FHIR: How HL7’s New API is Transforming Healthcare. Switzerland: Springer; 2018.
34. Cohen J, Kupferschmidt K. Countries test tactics in ‘war’ against COVID-19. Science. 2020;367(6484):1287-1288.
35. Ienca M, Vayena E. On the responsible use of digital data to tackle the COVID-19 pandemic. Nature Medicine. 2020;26:463-464.
36. Chen Y-D, Brown SA, Hu PJ-H, King C-C, Chen H. Managing emerging infectious diseases with information systems: reconceptualizing outbreak management through the lens of loose coupling. Information Systems Research. 2011;22(3):447-468.
37. Gostin LO, Hodge Jr JG. Personal privacy and common goods: a framework for balancing under the national health information privacy rule. Minn L Rev. 2001;86:1439.
38. Crowston K. A Coordination Theory Approach To Organizational Process Design. Organization Science. 1997;8(2):157-175.
39. Okhuysen GA, Bechky BA. Coordination In Organizations: An Integrative Perspective. The Academy of Management Annals. 2009;3(1):463-502.
40. Sengupta S, Calman NS, Hripcsak G. A model for expanded public health reporting in the context of HIPAA. Journal of the American Medical Informatics Association. 2008;15(5):569-574.
41. Gostin LO. Health Information: Reconciling personal privacy ith the public good of human health. Health Care Analysis. 2001;9(3):321-335.
42. Ness RB, Committee JP. Influence of the HIPAA privacy rule on health research. Jama. 2007;298(18):2164-2170.
43. Briand S, Mounts A, Chamberland M. Challenges of global surveillance during an influenza pandemic. Public Health. 2011;125(5):247-256.
44. Alessi C. The rights to privacy and citizen protection – A conflict between privacy and the public good? HealthcareITNews.com. 2020; May 6, 2020: https://www.healthcareitnews.com/blog/europe/rights-privacy-and-citizen-protection-conflict-between-privacy-and-public-good. Accessed 05/25/2020, 2020.

[1] Please see https://www.healthcaredive.com/news/cdc-to-launch-clinical-reporting-app-for-covid-19-in-may/576129/ for more details.

[2] e.g., https://ehrintelligence.com/news/key-piece-to-todays-interoperability-puzzle-cloud-fax

[3] e.g., https://www.cdc.gov/coronavirus/2019-ncov/php/reporting-pui.html

[4] Please see https://www.cdc.gov/ehrmeaningfuluse/Reportable-Conditions-Knowledge-Management-System.html and https://www.cdc.gov/nhsn/index.html for more details.

Kevin Schulman, MD, MBA, Clinical Excellence Research Center, Stanford University School of Medicine

Healthcare Providers Face Financial Pressures To Reopen

Healthcare systems in the United States are suffering from a drop in volume as patients have curtailed the use of non-emergent healthcare services. The economics of this abrupt drop in demand are severe – healthcare systems have large capital asset bases, employ large numbers of essential workers in clinical and non-clinical roles, and report single-digit operating margins. As a result of this highly leveraged business model, a downward shift in volume quickly moves financial performance from profit to loss. Thus, the pressure to reopen to patients is significant from the system perspective. But, from the patient point-of-view, it’s not clear if they share the same urgency.

Unfortunately, reopening the health care system will be more complicated than just switching on the “open” sign at the front door. The COVID pandemic has induced huge fear in the population about the virus and viral transmission. Hospitals are clearly at the epicenter of this fight, but stories about courageous healthcare workers becoming ill, or protesting the lack of PPE, have been daily headlines for two months. The public is concerned that they will be the next COVID case if they seek care; whether this concern has merit is irrelevant. At this point, it’s a fixed impression.

To Reopen, We Need to Build Trust

Interestingly, this is an issue that has emerged in a lot of our research in global health – how to build trust in the eyes of the consumer or patient that the healthcare system is there to help. It’s important to realize that rebuilding trust is the essential task before us as we consider reopening.

Take The Perspective of the Healthcare Consumer

From the perspective of the patient, how do I trust that I will not get sick from an in-person visit? Here, leaders need to see the world from the perspective of the patient in a way that they have never done before.

As a thought experiment, imagine that you are the patient seeking care, and every action and activity is governed by the thought that the last person before me to do the task was COVID positive. Do I want to “sign in” at the start of a visit? Will there be a queue to check-in and will there be social distancing? Will everyone be wearing a mask? Do I have to fill out a form on a clipboard or iPad? Will the waiting room be laid out to ensure that there is separation (and maybe separation of seniors for their protection)? Are the staff congregating together? Are the floor and surfaces clean? What about door handles and elevator buttons-how often are they disinfected (or are there convenient disposable tissues so I do not have to touch a surface). Who sat in the chair before me – was it cleaned before I sat down?

I know at Stanford we have had a lot of discussions about CDC guidelines and protecting staff, and these have sometimes been heated discussions as the recommendations have evolved. But in thinking about the patient, following CDC guidelines is a start but not sufficient. We need to overwhelm them with our attention to these issues if we want to win their trust. We don’t want a post on NextDoor that says I went to the doctor and I was scared – or worse, I got sick.

Examples from Disney and Retail

Interestingly, I was once involved with a discussion about Disney and their theme parks. We all think about the parks as entertainment, but to Disney, job one is not characters on parade but safety. Who wants to take their kids to a theme park where there is any risk of harm to your family?

You have probably never thought about the effort they put into safety, but I once saw it up close. One of my children jumped into a pool in front of me before they could swim. When the lifeguard saw this, they blew their whistle and jumped into the water – fortunately, I beat them to my child and didn’t need their service.

The remarkable thing was what happened out of the water. When they heard the whistle, all of the other lifeguards immediately shifted chairs to cover the quadrant of the lifeguard who went into the water. They were trained to do this to ensure that there were not two victims that needed to be rescued. Now, that was attention to safety.

We’ve seen this all around us as businesses have evolved their internal policies and procedures to respond to COVID. My family members have remarked how clean supermarkets are now, how the aisles are one-way, how the carts are wiped down between uses, how we have social distancing markers everywhere, how the check-out clerks are protected, and how you don’t have to touch anything to check out.

Healthcare: Attention to Detail

For a successful reopening, we’ll need to adopt this level of attention to detail. And we will need to message the patients about our efforts – we’re already two months behind supermarkets in their learnings.

This will take engagement from senior leaders and line staff. Leaders and managers will have to walk the floors to ensure that patient safety is enforced throughout the organization. We’ll have to revisit SOPs to modify them for new COVID procedures and train the staff on the new policies. This would be a great time to recruit feedback from the front lines, and from patients, about ways in which we can make our system even safer for patients. Staff and providers should acknowledge the fear, explain the new procedures, and thank patients for their courage.

One delicate issue will be our newfound expertise in virtual visits. We should be careful that we maintain this capability until we rebuild trust in in-person visits, despite the economic pressures to curtail these services. Forcing patients to come in before they are ready would undermine the trust being built through all of these other actions. Indeed, rather than cut back on these new virtual services, we should build them into our standard operating model.

Looking Forward

It is not clear how long this COVID challenge will last; it could be months if we have a vaccine or years if we do not. Building trust needs to be part of the reopening plan for this new normal. The work needs to evolve based on local circumstances and progression of the epidemic.

Will Mitchell, Anthony S. Fell Chair in New Technologies and Commercialization & Professor of Strategy, Rotman School of Management, University of Toronto

Life Sciences Companies Are Key To Creating COVID-19 Solutions

Many pharmaceutical firms and other companies in the commercial life sciences sector are actively working on short-term and long-term solutions to COVID-19. Firms are testing potential treatments, developing vaccines, producing tests, providing support services, and undertaking many critically important innovations for the pandemic.

Many of these attempts will fail – that is the nature of both science and the market – but even now some innovations are emerging as credible parts of the solution. Successes include the apparent value of using Gilead Science’s remdesivir, an investigational nucleotide analog that has demonstrated at least some therapeutic impact against the virus that causes COVID-19. Remdesivir has received emergency use authorizations in both Japan and the U.S. [1] Other successes in treatments, vaccines, testing, and other services will undoubtedly emerge from other firms.

The successes raise the issue of pricing, particularly for pharmaceutical-related products. How much should companies charge for critically important therapies? In the case of remdesivir, for instance, pricing proposals have ranged from PublicCitizen arguing for $1 a day [2], which the organization believes would cover the incremental costs of the drug, to an Institute for Clinical Effectiveness Review that suggests a price of about $4,460 per patient.[3] Investors may suggest even higher prices for remdesivir and other treatments.

Long-Term Pricing

In the long term, the answer to the pricing question is straightforward. Life sciences companies need to charge prices that cover both the fixed and variable costs of their businesses, as well as generate sufficient profits to attract ongoing investment. Simply recovering the variable costs of producing and selling a successful drug is not enough: the mix of prices that a company charges needs to cover the substantial sunk costs of investing in both failed and successful projects.

In the pharmaceutical market, this means having multiple list prices that reflect value ceilings in different markets and then negotiating discounts and rebates that manifest the bargaining power of payers in those markets. This is the pricing strategy that firms use today. [4]

The traditional pricing strategy is controversial among payers, consumers, and politicians – and has been controversial since the 1960s, at least. Yet it may be more obvious now during the pandemic why we need companies with sufficiently robust technical, organizational, and financial strength to be able to respond to unexpected demands such as the virus. If we pare profits to the bone with overly-demanding pricing regimens, we damage the companies’ ability to respond when needed.

But what of the short term? Should Gilead and other companies offer more deeply discounted “crisis prices” in the face of the COVID pandemic? We do not suggest that Clorox reduce the price of bleach, which is an essential part of responding to the virus, even though Clorox’s stock price rose more than 30 percent between the beginning of February and mid May. Should we ask life sciences companies that are at least as vital as Clorox for COVID solutions to provide lower than normal prices?

Short-Term Pricing

I will argue here that there is financial and strategic value in setting lower short term crisis prices, at least from established life sciences companies. At the same time, there are strong boundaries to that argument.

Strategic value

First, life sciences firms gain strategic value by being financially responsive to the current situation, where the responsiveness may help reinforce current gains in public views of the industry. In 2019, the pharmaceutical industry fell to the bottom of the Gallup industry reputation ratings, with a net positive score of negative 31. [5] Since the beginning of the coronavirus crisis, the industry faces competing dialogues: some signs of more favorable perceptions [6] countered by predictions of profiteering. [7] Offering crisis-priced treatments during the pandemic would help reinforce the positive sentiments.

It is not clear that such positive PR is essential. Established firms in the life sciences industry have flourished despite decades of criticism. One approach would be to follow traditional pricing strategies and once again simply attempt to ride out the complaints. Yet the potential challenges of damaging price controls in the U.S. and in many other countries has never been higher: the claim that pharmaceutical prices need to be lower may be the only point of agreement among politicians from otherwise diametrically opposed viewpoints.

In addition, there is potential for countries to invoke compulsory licensing rules within the agreement on Trade-Related Aspects of Intellectual Property Rights (TRIPS) of the World Trade Organization. Indeed, countries such as Chile, Israel, and Ecuador are already considering such actions [8], while the World Health Organization in May reinforced the potential for bypassing patents to deal with COVID-19 needs.[9] Crisis-pricing may reduce the incentives to invoke compulsory licensing. AbbVie, for instance, in March announced that it would remove patent-related limitations on the production and use of its HIV/Aids drug, Kaletra (lopinavir/ritonavir), which is being investigated for potential use against COVID-19. [8] Similarly, Novartis in March announced that it would donate more than a hundred million doses of hydroxychloroquine, which is being investigated as a possible treatment. [10]

In parallel with heightened political pressure, the life sciences industry is facing increased negotiating power on the part of newly-created combinations of health insurers, specialty pharmacy services, and pharmaceutical benefit management firms. [11] Reinforcing the current gains in positive reputation – and blunting the inevitable attacks – will provide negotiating points when these demands resurface after the pandemic has passed. Thus, there is strategic value in setting below-normal prices for COVID-related innovations during the pandemic.

Financial value

Second, there is financial value for the firms in providing short-term ceilings on prices. The goal of a profitable pricing strategy is to offer differential prices that meet the willingness and ability to pay of each market segment. So long as these differential prices exceed the variable costs of production and distribution and there is no parallel trade between segments, this strategy is profit maximizing. Such differential pricing is also a “fair” price, in the sense that no payer is forced to pay more than they are able and willing.

The pandemic has produced the difficult situation where the need for COVID-19 treatments is huge, yet financial ability is more constrained than ever. Even in wealthy countries such as the U.S., consumers and third-party payers that traditionally had the ability to accept higher prices for valuable medicines are facing immense financial pressures in the face of reduced economic activity and deep losses.  Attempting to enforce prices near the “value ceiling” in such cases will inevitably mean rationing of treatments to those who can afford the higher costs.

In turn, in addition to ethical concerns about limited access, the rationing will mean lost sales for the companies as those who cannot afford the higher prices are left out of the market and either do not receive the treatments or need to depend on unpaid compassionate care programs. How the trade-off between price and volume will affect net revenues depends on price elasticity; nonetheless, there will be real limits on profits. Hence, a lower than normal price at this point will lead at least to less reduction on in bottom line profits than in “normal” times and might actually be profit-maximizing.

Gilead is a useful example here. The company has extensive experience with differential pricing. Its long-established antiretrovirals have been offered at different markets, including many low- and middle-income countries, at prices that suit the ability to pay in individual markets. Gilead’s hepatitis C treatment Sovaldi (sofosbuvir) and its follow-on drugs have been listed in different markets at prices that are relevant for those markets.

Indeed, even in “high price” markets such as the U.S., Gilead faced strong pressures from many payers for steep discounts from list prices of $75,000 or more for hepatis C treatment regimens, with discounts commonly reaching or exceeding 50 percent as competing drugs entered the market. Indeed, competition from drugs such as AbbVie’s Mavyret (glecaprevir/pibrentasvir) and Gilead’s own authorized generic brought U.S. list prices for hepatitis C treatments down to under $25,000, even before rebates and discounts. The point here is that high price windows even for highly innovative medicines often are short.

For coronavirus treatments, Gilead is rapidly moving to an active differential pricing strategy for remdesivir. The company has already signed agreements with generic manufacturers in India and Pakistan to supply the drug in 127 countries, with the generic partners setting prices based on local market conditions. [12] In this agreement, Gilead will not receive royalties as long as the World Health Organization maintains emergency status for COVID-19. This strategy of out-licensing production and sales for lower income countries is one that Gilead has used previously with HIV/Aids and hepatitis C drugs, including providing licenses to the Medicines Patent Pool. The strategy leads to broad availability of the drugs globally and contributes to Gilead’s financial success by providing sales that it would otherwise not be able to achieve.

Hence, “crisis prices” for COVID-19 solutions offer benefits for both strategic public perceptions and financial bottom lines.

Boundary Conditions: Newer Firms And Longer Term Expectations

Two caveats are important here, concerning newer firms and managing longer term expectations.

Newer firms need financial returns now

First, the argument above applies to established life science firms that can afford to take a short-term hit on their bottom lines. Fortunately, many of the companies that are devoting enormous time and money to developing treatments, vaccines, tests, and support are profitable established firms. They will not risk failing or struggle to gain new investments if they offer lower prices during the pandemic.

Yet many innovative companies do not have this ability. Instead, many of the firms that are active in developing potential solutions to COVID-19 are early-stage companies that do not have large reservoirs of retained income. Rather, they are still at the stage of large accumulated losses.

For example, consider Moderna Inc., which has received highly visible publicity about its work on a potential coronavirus vaccine.[13] This work has completed multiple phase 1 studies in several countries and is now entering phase 2 clinical testing. [14] The company’s vaccine might or might not succeed, but it is one of the efforts that has a chance of paying off. In the four years ending December 31, 2019, though, Moderna has lost $1.37 billion in negative net income as it has invested in developing new therapies and vaccines.

Moderna is a clinical-stage biotechnology company that was founded in 2010 and went public in 2018. The company has research relationships with long-established major pharmaceutical companies such as AstraZeneca and Merck; with more recent life sciences companies such as Vertex; with public agencies such as the Biomedical Advanced Research and Development Authority; with universities such as Harvard; and with civil society organizations such as the Gates Foundation. Funding for its research has come from contracts with these organizations and from investors’ equity. Current investments in the company, including a planned secondary offering in May 2020 that seeks to raise about $1.25 billion, reflect investors’ bets on both clinical success and future profitable prices.

Companies like Moderna that lack the resources that come from prior profitability– and there are many others in the same situation – do not have the financial strength for sustained crisis pricing. Novavax and Dynavax with potential vaccines, Sorrento with antibody research, and Vir Biotechnogy with potential treatments are just a few of many such examples. If we want the benefits of these companies’ technical and organizational skills in fighting the COVID-19 pandemic and potential future crises, we need to be willing to pay for their successes.

Dealing with this issue will be challenging. One solution is for the newer firms to receive subsidies from established life sciences partners, foundations, and public agencies to help counter-balance crisis prices. A second is simply to hope that investors will continue to provide equity with the expectation that the firms will become profitable once the crisis passes. A third is for third-party payers to bite the bullet and be less demanding about discounts from such newer firms. No matter what the route, though, if we want the benefits of the successes, we will need to find a way to pay for them.

All firms need future profits to sustain their financial strength

Second, there is a real risk that some people with view “crisis pricing” as a “new normal”. That is, some will conclude that if companies can afford to offer lower prices during the pandemic, they can continue to do so.

Yet the reason that established life sciences companies are able to offer crisis pricing is their historical profitability. Our payments for drugs in the past have created the financial strength that has allowed investments in technological and organizational capabilities that are producing the successes we need to solve the coronavirus crisis. If we are not willing to return to sustainable pricing policies, then we will undercut our ability to respond to future crises. Any crisis pricing needs to come with the recognition that we will return to normal pricing strategies – and, ideally, do so quickly.

Looking Forward

I will not suggest a particular price for any drug or product. Firms have many options: Lower list prices, deeper discounts, global licensing, and extensive donations are just a few of the possible ways to provide “crisis prices”.

Rather than focusing on any one mechanism, the core argument here is that multiple forms of crisis pricing are relevant during the COVID-19 pandemic. The lower than normal prices will help life sciences companies turn the wheel on their negative reputation, providing a strategic win. They can be part of a thoughtful differentiated pricing strategy, providing a financial win. And, most importantly, they will help as many people as possible gain access to the innovative solutions that strong life sciences companies are helping to create.

Yet, we need to be careful about recognizing the context for any crisis pricing strategy. The very ability to offer such prices derives from the financial strength of the companies that are creating solutions. If we want crisis prices during the next crisis, we need to be willing to invest in the companies that will create the solutions. The traditional pricing strategy in the life sciences sector is a central part of those investments.

 References

[1] https://www.drugs.com/history/remdesivir.html

[2] https://www.citizen.org/news/gilead-should-price-remdesivir-at-1-per-day/

[3] https://icer-review.org/material/icer-covid-models/?utm_source=ICER_COVID_model&utm_campaign=ICER-COVID_model&utm_medium=email

[4] Will Mitchell. 2018. Pharma Prices Are Not Too High (Usually). Health Management Policy and Innovation, Volume 3, Issue 2. HMPI.org

[5] https://news.gallup.com/poll/266060/big-pharma-sinks-bottom-industry-rankings.aspx

[6] https://www.fiercepharma.com/marketing/pharma-industry-public-perception-picking-up-around-covid-19-new-poll-indicates-first

[7] https://www.nytimes.com/2020/03/18/opinion/coronavirus-vaccine-cost.html

[8] https://medicineslawandpolicy.org/2020/03/covid-19-and-the-come-back-of-compulsory-licensing/

[9] http://freepdfhosting.com/b97c3bc51f.pdf

[10] https://www.novartis.com/news/media-releases/novartis-commits-donate-130-million-doses-hydroxychloroquine-support-global-covid-19-pandemic-response

[11] Navid Asgari and Will Mitchell. 2020. The Evolution of value chain integration in the U.S. pharmaceutical ecosystem. Health Management, Policy, and Innovation, Volume 5, Issue 2 (forthcoming).

[12] https://www.fiercepharma.com/manufacturing/gilead-sciences-inks-licensing-agreements-to-produce-covid-19-therapy-remdesivir-for

[13] https://www.nytimes.com/2020/05/07/health/coronavirus-vaccine-moderna.html

[14] https://www.modernatx.com/modernas-work-potential-vaccine-against-covid-19

Aaron Baird, Georgia State University, Kellen Mermin-Bunnell, Yale University and WellStar Health System, and Jason Lesandrini, Wellstar Health System

Contact: Aaron M. Baird abaird@gsu.edu

What Is Contact Tracing?

Contact tracing includes two primary processes: (1) case identification and investigation, and (2) proximity tracking, alerting, and follow-up.¹ Contact tracing is used by public health officials to slow the spread of an infectious disease through investigation of confirmed or suspected cases, notification, follow-up, and potential quarantining of exposed contacts during the disease incubation period.² This approach, while potentially effective in reducing the spread of infectious diseases, particularly during a pandemic, is labor intensive and requires significant public health resources, political will, and public cooperation.

What is digital contact tracing?

An alternative to in-person contract tracing, requiring less intensive public health resources, is digital contact tracing. Digital contact tracing is based on the same fundamental principles as contact tracing, but instead relies upon the use of digital technologies, such as smartphones and electronic case reporting, to trace the paths of individuals and generate options for alerting those who may have come into proximity (“contact”) with positive or suspected cases.³ For example, mobile contact tracing was effectively used during the 2014-2016 Ebola epidemic in Sierra Leonne where challenges with paper-based contact tracing systems were overcome using a pilot smartphone app.⁴

Digital contact tracing for COVID-19 cases has begun to be used in countries such as South Korea, China, and Singapore – and is beginning to be debated in many others, including the U.S.^5-7 For instance, Apple and Google have recently announced the development of tools that could be used to facilitate the development of contact tracing applications.^[1] The CDC has also announced the coming availability of an electronic case reporting application, which will enhance the ability of health care providers to report positive or suspected cases of COVID-19,^[2] and has posted preliminary criteria for the evaluation of digital contact tracing tools for COVID-19.^[3]

While digital contact tracing has significant potential for leveraging health information to reduce the spread of COVID-19 in a timely and efficient way, it raises ethical concerns. As benefits, contact tracing for COVID-19 has allowed many businesses to stay open while also facilitating quarantining of positive cases and those who may have come into contact with cases.⁶

Nonetheless, concerns arise because, while efforts have been made to ensure privacy and prevent exploitation or inappropriate use of the digital contact tracing system and the health information it consumes and generates, full anonymization is not always possible. As a result, unintended consequences are already occurring. One example is inadvertently identifying individuals who are positive or suspected to be of high risk with only minimal effort. Another is expanding the scope of the system to include additional personal data such as travel and credit card information to determine individual proximities, such as whether one ate in the same restaurant as a suspected case at the same time.^6,7

Possible Use of Digital Contact Tracing by U.S. Employers

U.S. employers might consider digital contact tracing as employees return to work in-person in order to: 1) reduce SARS-CoV-2 transmission; 2) enhance employee and customer perceptions of safety; 3) reduce liability risks; and 4) proactively mitigate future insurance premium increases, especially if the employer is self-insured. Thus, an outstanding question is whether or not employers will require, or strongly suggest, that returning employees not only be tested for COVID-19, but also participate in transmission reduction efforts by installing a digital contact tracing application on their phones. Such applications could then alert the employee, the employer, or public health professionals of potential exposure to a positive or suspected case of COVID-19.

Mitigation actions could then be taken. For example, given that COVID-19 was declared a national public health emergency in the U.S., public health officials have special authority to implement quarantine measures for high-risk individuals.

We are only speculating as to what U.S. employers may require or encourage of their employees as they return to work at physical locations. Digital contact tracing may not end up being used widely in the U.S. or by U.S. employers. However, the potential use of digital contact tracing raises ethical questions that should be proactively addressed to the extent possible. For instance, if such an approach is implemented by employers as employees return, digital contact tracing might be managed as part of “wellness” programs.

The focus and messaging of such programs will likely be on employee safety and enhanced customer safety. Both staff and clients may be unwilling to go to an organization that is not actively monitoring their team members.

However, challenges with COVID-19 are that testing, case identification, and reporting are still highly varied, and that individuals can be asymptomatic carriers. Further, effective prevention might necessitate the rapid expansion of initially narrow approaches into more ethically dubious domains, such as the expansion of digital contact tracing to include non-work hours and routines.

We do not take a position on whether or not digital contact tracing use by employers is appropriate or ethically supportable. Rather, we identify fundamental ethical issues that should be addressed if such an approach is considered or implemented. We specifically focus on U.S. employers.

Digital Contact Tracing Ethical Considerations and Implications

Three primary ethical issues associated with potential digital contact tracing use by U.S. employers are: 1) employee digital contact tracing choice, such as opt-in vs. opt-out vs. mandated; 2) COVID-19 case and proximity information quality; and 3) COVID-19 health information use and rights.

Ethics of COVID-19: Employee Digital Contact Tracing Choice

Will digital contact tracing application, installation, and use be mandatory for employees returning to physical work locations? Take, for instance, a health care organization that begins to re-open clinics, departments, and operating rooms that were previously closed. Employees returning to such environments may need to be tested and cleared for COVID-19 to initially return to work, especially given the rate of COVID-19 spread among healthcare workers.^[4] However, if the employees come into contact with a positive or suspected COVID-19 case after being tested, either while at work, on the way to work, or in their personal lives, they put patients and other employees of that organization at risk.

A digital contact tracing application could be used in all settings or only while the employee is at work to ensure that those in contact with COVID-19 cases, such as ICU providers and staff, do not unintentionally interact with other employees. Such a program could be initially implemented on a phone or device supplied by the organization and even left at the organization when the employee is not present, such as when they go home or out to lunch. The challenge, of course, is that the employee is likely to come into contact with other potential COVID-19 cases outside of the workplace, which creates significant risk for those who come to the organization in-person.

Thus, one underlying ethical challenge is addressing how much choice returning employees should have when determining whether or not to use, as well as when to use, digital contact tracing. Will such use be mandated and, if so, will such mandates only apply while in the work environment? If digital contact tracing use is not mandated, will it be encouraged or even coerced through wellness or safety programs?

A concern which will need to be addressed, like other employee wellness programs, is whether employees are given an incentive to participate in the program. Providing incentives can run afoul of unduly influencing employees to participate, hence undermining the voluntariness of such programs.⁸ Further, if usage is voluntary, either through opt-in or opt-out programs, technology access and sample selection are likely to be issues. For instance, will employees be required to use the app on their personal phones? What if they do not have a personal phone or would prefer not to use the app on personal devices?

Employers should avoid implementing programs that exacerbate existing health disparities. What if only healthy employees opt-in, while employees who may be positive opt-out in order to be able to return to work or to avoid potential stigma? Would all employee population segments be equally encouraged to opt-in or would certain populations, such as those with chronic conditions, be more encouraged to opt-in? The challenge here, as with other ventures in health care, such as the Affordable Care Act, is that if a sufficient number fail to opt-in, the impact of digital contract tracing will be significantly decreased.^3,5

Ethics of COVID-19: Case and Proximity Information Quality

COVID-19 raises strong challenges. It can be difficult to identify positive, infectious cases due to testing constraints. Questions exist regarding whether or not previously positive cases could now have sufficient antibodies to prevent future infection.

Additionally, while efforts are being made to improve health information fragmentation in our health care system,^9,10 t aggregating high-quality health information remains a significant challenge, especially when individuals visit multiple healthcare providers. Further, it is unclear how to best standardize proximity data, including proximity distance and duration.

Identification of exposures to asymptomatic cases will be especially challenging. Even if we assume high accuracy and reliability of proximity tracing data, the accuracy and reliability of COVID-19 case data will be highly variable, at best, for some time. Further, it is not known whether digital contact tracing applications will only rely on objective data, such as results from approved lab tests, or will also rely on subjective data, such as the self-reporting of symptoms or suspected cases. If subjective data is also used, the accuracy and reliability of such data is likely to be even more varied and raise even more ethical questions.

Therefore, another primary underlying ethical issue is that of the quality of the information that serves as the input to digital contact tracing. Particularly concerning is proximity and case information quality, as well as the quality of corresponding health information that might be used to identify high risk cases. For instance, will there be consistent benchmarks for being flagged as either positive or having crossed paths with a person who has been flagged? Is self-reported information adequate or does it need to come from a clinician or confirmed test?

The nature of the information raises concerns. In non-pandemic time periods, employers do not have access to an individual employee’s protected health information. We recognize there are times that medical information about an employee is needed, particularly with regard to whether they can complete assigned work, and perhaps COVID-19 status is one piece of that medical information. However, an argument needs to be offered to justify sharing that information with the employer.

Further questions arise. How do we address false positives and challenges of clearing stigma if falsely identified? What if an employee self-reports as positive in order to be able to work from home, but is not actually positive? What if an employee who is positive self-reports as negative, or does not report, to avoid potential consequences or stigma, possibly due to the potential for discrimination against groups with pre-existing conditions?

Experience from other employer-sponsored programs shows that groups can be stigmatized. For instance, disabled individuals are often stigmatized in employee wellness programs as never being able to be considered “well enough” to meet criteria for the program.^8,11 Even further, will the absence of alerts, such as not receiving a proximity alert, provide a false sense of security?

Ethics of COVID-19: Health Information Use and Rights

While we have strong individual rights for identifiable health information in the U.S., we also have enforceable laws requiring reporting of notifiable diseases and conditions to public health agencies. Such identifiable information associated with notifiable diseases and conditions has traditionally been kept private by public health agencies and then reported in the public domain either in aggregate or in other non-identifiable ways.

We do not anticipate health information rights laws or norms to be changed or violated during COVID-19. Nonetheless, mediation via a third-party application, potentially not covered by HIPAA, raises important ethical concerns. This may be especially troubling if employees are self-reporting data.

Fundamentally, this is a debate over COVID-19 health information rights and allowances for use. For instance, who has rights and access to digital contact tracing information? How would stigmatization or identification be avoided? If an employer obtains information that an employee is positive, could this impact insurance rates, work requirements (e.g., must work from home), or access to resources (e.g., can’t come into the office)? It may even result in employers wanting employees who return after being positive to be on the “front-lines” as they are assumed to have antibodies.

Consider the following scenario. What if an employer doesn’t have rights or access to the information, an employee tests positive, but then does not report that or that information does not come up via the digital contact tracing app? Is liability then a concern either for the employee or for the employer?

Resolving Ethical Tensions 

As with any ethical tension, resolution requires a systematic process. While numerous frameworks exist, the American College of Healthcare Executives (ACHE) provides a useful example (Table 1).¹²

Take for example applying this framework to the ethical issues associated with “Ethics of Employee Digital Contact Tracing Choice.”

Step 1: Circumstances. Systematic ethical evaluation, conducted during assessment of potential digital contact tracing implementation, should begin with recognition of potential ethical conflicts and establishment of facts. For example, employer preferences and employee perceptions may not be fully aligned. Before determining the best course of action, however, facts need to be established. For instance, what is the incidence rate of COVID-19 in the community, amongst employees currently, how is the disease spread, and how might it impact our team members? An employer should ensure to collect as much background information as initially possible in order to inform decision making and enable ethical evaluation.

Step 2: Question. While specific ethical questions may vary by organization and situation, the individuals participating in the process should make sure that the question is an ethical one and not something else, e.g., a question of compliance, law, or clinical process adherence. In this context, specific questions might be: Should employers implement digital contact tracing? How much freedom should employees have in adopting it?

Step 3: Principles and values. The organization should then consider who is a stakeholder in the situation, e.g., the employee, employer, consumers, and community, and what values they bring to the discussion. Often important in these contexts, the organization itself should be considered a stakeholder and the values of the organization should be considered alongside other stakeholders. One should always ask if the approach being considered is consistent with the organizational values.

Step 4: Options. Then, options and what values each option promotes should be considered. In most ethical matters, there will be risks and benefits to promoting one value over another. One option in this case is to mandate adopting the app for all employees in all settings, i.e., an app that is active irrespective of whether the employee is at work or not. The employer should consider the benefits to this, such as being able to ensure all points of contact with the employee and burdens endured as well as whether or not employees may perceive their individual freedom as being unduly overridden and subsequently choose not to work for the employer.

Step 5: Select. Finally, after weighing the arguments for and against each option, a choice should be recommended using the framework of, “We made the decision to _____ because doing so promotes this value______.” As applied to this case, “We made the decision to mandate adoption of digital tracing app because doing so promotes the safety and protection of our employees and community.”

Step 6: Anticipate. As with other ethical situations, employers should consider how future ethical conflicts associated with digital tracing could be avoided. If the organization believes mandated tracing is the ethically appropriate option, they should, for example, inform potential future employees of their decision and the justification for it. Doing so enables future employees to make an informed choice regarding whether to join the company. Finally, the organization should develop a resolution processes for ethical conflicts that emerge during program implementation and use, such as may arise when needing to balance individual and community rights.

Finally, for those who have access to ethics resources, such as an ethicist on staff, an ethics committee, or a connection with local academic institution’s ethics center, working with them on these issues is beneficial to organizations, employees, and even customers. We recognize that COVID-19 has brought to light ethical issues businesses are just now having to face. Having an ethics resource available to them is now more important than ever as leaders confront these challenges.

Conclusion

Digital contact tracing has significant potential for reducing the spread of SARS-CoV-2, as both a substitute and a complement to public health resources currently available. While digital contact tracing for COVID-19 is unlikely to be mandated for all U.S. citizens and residents, U.S. employers, seeking to enhance safety, reduce liability, and proactively address potential insurance premium increases, may mandate or coerce returning employees to use digital contact tracing applications.

This paper has analyzed these issues from an ethical perspective. We identified and discussed ethical issues and implications of: 1) employee digital contact tracing choice such as opt-in vs. opt-out vs. mandated; 2) COVID-19 case and proximity information quality; and 3) health information use and rights. Ethical considerations need to play a central role in the potential planning for implementation of digital contact tracing by U.S. employers.

References

1. CDC. Digital Contact Tracing Tools for Covid-19. 2020; https://www.cdc.gov/coronavirus/2019-ncov/downloads/digital-contact-tracing.pdf.
2. Hellewell J, Abbott S, Gimma A, et al. Feasibility of controlling COVID-19 outbreaks by isolation of cases and contacts. The Lancet Global Health. 2020;8(4):e488-e496.
3. Waltz E. Halting COVID-19: The Benefits and Risks of Digital Contact Tracing. In. IEEE Spectrum2020.
4. Danquah LO, Hasham N, MacFarlane M, et al. Use of a mobile application for Ebola contact tracing and monitoring in northern Sierra Leone: a proof-of-concept study. BMC Infectious Diseases. 2019;19(1):1-12.
5. Ferretti L, Wymant C, Kendall M, et al. Quantifying SARS-CoV-2 transmission suggests epidemic control with digital contact tracing. Science. 2020;eabb6936(31 Mar 2020):1-12.
6. Kim MS. Seoul’s Radical Experiment in Digital Contact Tracing. In. The New Yorker2020.
7. Yasheng Huang, Meicen Sun, Sui Y. How Digital Contact Tracing Slowed Covid-19 in East Asia. In. Harvard Business Review2020.
8. Madison KM. The risks of using workplace wellness programs to foster a culture of health. Health Affairs. 2016;35(11):2068-2074.
9. Mandl KD, Kohane IS. A 21st-Century Health IT System-creating a real-world information economy. N Engl J Med. 2017;376(20):1905-1907.
10. Braunstein ML. Health Care in the Age of Interoperability: The Potential and Challenges: Part 1. In. IEEE Pulse: IEEE; 2018.
11. Plump CM, Ketchen Jr DJ. New legal pitfalls surrounding wellness programs and their implications for financial risk. Business Horizons. 2016;59(3):267-272.
12. Nelson W. Making Ethical Decisions. Healthcare Executive. 2015;30(4):46-48.

^[1] Please see the following for more details: https://techcrunch.com/2020/04/23/first-version-of-apple-and-googles-contact-tracing-api-should-be-available-to-developers-next-week/, https://www.theguardian.com/world/2020/apr/21/france-apple-google-privacy-contact-tracing-coronavirus

^[2] Please see the following for more details: https://www.fiercehealthcare.com/tech/cdc-plans-to-roll-out-reporting-app-for-covid-19-cases-may

^[3] Please see the following for more details: https://www.cdc.gov/coronavirus/2019-ncov/downloads/php/prelim-eval-criteria-digital-contact-tracing.pdf

^[4] Please see https://www.pbs.org/newshour/health/health-care-workers-are-10-20-of-u-s-coronavirus-cases for more details.

Ranjith P V, Associate Professor of Decision Science, CMS Business School, JAIN Deemed to be University; Uma Warrier, Professor & Area Chair, CMS Business School, and Chief Counsellor, JAIN Deemed to be University; and Aparna J Varma, Associate Professor of Marketing, GSSS Institute of Engineering & Technology for Women

India is Using a Complete Lockdown to Combat COVID-19

COVID-19 has now affected more than 27 lakh (2.7 million) people worldwide, with over 200,000 deaths being reported. Countries such as Italy, Spain, the U.S., Ecuador, and others have struggled to find ICU beds to accommodate serious patients. The epidemic is now threatening many other countries around the world.

Different countries are using different methods for controlling the epidemic. At this point, almost half of the world is under at least partial lockdown, affecting the livelihood of millions. At the same time, other countries such as Germany and China are beginning to open up their economies.

India, with a population close to 1.3 billion, provides a striking example of strategies for dealing with the crisis. On March 24, the county initiated a complete lockdown, initially for three weeks and recently extended to a total of 40 days. The lockdown has helped the country check the number of cases, which as of April 26 had not reached about 27,000 identified cases and less than 1,000 attributed deaths.

Most places in India are still in tight control due to lockdown, although some opening is beginning. India has divided locations into red, orange, and green zones based on the number of cases, with green being minimum. The government has started to provide some relaxations in green zones, while requiring social distancing to help economic activity recover. As the days go by, the hope is that more zones will become corona free and they will go back to normalcy.

Healthcare Innovations during the Lockdown

During the lockdown, rather than counting on full containment, India is finding innovative ways to build healthcare infrastructure. Here are examples of ways that India is dealing with the disease.

1. Converting railway coaches into isolation wards. Railways are converting 20,000 coaches into isolation wards to accommodate 320,000 people. The converted coaches include key ICU facilities such as ventilators, as well as bathrooms and other basic facilities. There is space for temporary accommodation for doctors and nurses, along with space for them to carry out clinical work. These arrangements will help the country accommodate more patients if treatment is required. [1] , [2]

The railway coach conversion strategy faces challenges.[3] Converting toilets into bathrooms, attaching oxygen cylinders, and modifying the coaches to carry medical equipment is complicated. The conversions involve older coaches, leaving newer air-conditioned coaches available when passenger traffic resumes. Maintaining hygiene is not easy. Nonetheless, the conversions are an important step to ensure that patients can be treated if the pandemic spreads in the country.

2. Converting stadiums into quarantine and hospital facilities. Plans are under way to convert Jawaharlal Stadium in New Delhi and Gachibowli Stadium in Hyderabad into quarantine facilities and, if needed, treatment centres. Similarly, there is a plan to convert Sarusajai Stadium in Assam into a quarantine centre.
3. Converting hotels into quarantine centres. Several hotel rooms in the country are being converted into quarantine centres. The owners are paid for this and basic amenities are given to the people who are being quarantined.
4. Deities. In rural villages and smaller towns, police officers and other officials are dressing up as Yamaraja, the Hindu god of death, to promote the lockdown. They are then talking with people to highlight awareness of the dangers. Their goal is to make sure that people do not roam around in the streets during the lockdown.
5. Mobile testing buses. Mysuru, which is a district of Karnataka State, is using Road Transport Corporation buses as mobile sites for testing COVID-19 cases in different parts of the region.
6. Plasma therapy. The Indian government has allowed plasma therapy to be used for critical hospitalised patients on a trial basis, hoping that it will support speedy recovery.
7. Private labs. Private labs in India are now allowed to test for COVID-19, which is helping find more cases before they spread to others.

Implementation Challenges

The steps in place should ensure that enough isolation wards and quarantine centers are available for tackling a possible spurt in COVID-19 cases. Nonetheless, multiple challenges remain.

1. Medical staff availability. As well as facilities, it is necessary to ensure that enough medical care professionals are available to care for hospitalized cases.
2. Protecting medical staff. It is critical that health care staff do not themselves contract the virus. Indeed, there are signs that medical teams in some parts of the country are already infected with the disease.
3. Climate. It remains to be seen how effectively train coaches that lack air conditioning can be used in the hot climate.
4. Testing. Despite the expansion in use of private labs, India needs increased ability for coronavirus testing to prevent widespread contraction of disease.
5. Vaccination: Once effective vaccines emerge from global development efforts, which might occur by the end of the year or might take multiple years, India will need to ensure that vaccinations are widely available in the country.

Looking Forward

These strategies are very recent and only time will tell whether they are successful. Future actions after lockdown will determine how successful we will be against the disease. It will not be easy for a country with such a huge population to combat such a dangerous pandemic.

Clearly, it will take a huge effort from all parties to make sure that this pandemic is erased from the society and economic activity and normal life are restored. But if we can ensure social distancing along with effective testing, tight control over the movement of people, efficient clinical management, innovative ways of creating awareness, and ensuring maximum preparedness of the health infrastructure, then it is likely that India will come out of this phase with limited damage.

Funding: No funding was used for the article.

Originality: The article is original in nature and not copied from any other research work.

References

[1] Alluri Aparna ,How India’s behemoth railways are joining the fight against COVID-19, BBC News, 9^th April 2020

[2] India turns trains into isolation wards as COVID-19 cases rise, Aljazeera News, 2^nd April 2020

[3] Mamuni Das, COVID-19: The Pros and Cons of using train coaches as isolation wards, March 27,2020

Jianhong Wu, PhD, Professor, University Distinguished Research Professor of Mathematics, York University 

COVID-19 and Influenza May Be Confused with Each Other

As global public health authorities exert their best efforts to contain the COVID-19 pandemic, researchers are trying to determine whether the disease will be eradicated, naturally attenuate and become endemic, or, like influenza, become seasonal. Determining the fate of SARS-CoV2 has crucial public health and societal implications. A future COVID-19 outbreak that coincides with flu season could conspire to overwhelm and strain health-care facilities, especially in resource-limited settings, and increase infection transmission within these facilities.

Clinical decision-making and public health responses are likely to be hampered as the distinction between the two infections can be challenging given the non-specific and often overlapping symptoms of influenza and COVID-19. Moreover, there still exists a high level of uncertainty around the clinical manifestation and progression of the infection and disease. This uncertainty is currently exacerbated by the lack of widespread rapid diagnostic tests that would provide quick and accurate diagnosis.

When a new outbreak occurs, the implementation of public health measures — including case detection and contact tracing, self-isolation, social distancing and quarantine — is challenged by the dearth of clinical and epidemiological data available in real-time. Delays of months or even years for effective treatments or vaccines to be discovered, tested, approved and, finally, made commercially available are likely. Furthermore, defining cases can be non-trivial, given the non-specific features of  COVID-19, thus limiting downstream public health efforts to identify and effectively manage the disease.

According to some experts, the virus responsible for the COVID-19 had already been circulating much earlier than late December 2019 but the exposure of such a pathogen was masked by a high number of people complaining of influenza-like-illness. Therefore, it is critically important to be able to identify and prevent influenza.

Two Benefits of a Mass Influenza Vaccination Campaign

The current vaccination coverage rate for influenza is still sub-optimal, about 42% among adults 18 years and older in Canada vs the national target of 80%. In turn, due to the non-specific features of most respiratory infections, we believe that a mass influenza vaccination campaign implemented well before the onset of the flu season will have two benefits. First, it will contribute to the exposure of emerging/re-emerging respiratory pathogens and to their quicker identification. Second, it will facilitate the design and implementation of relevant public health interventions, by decreasing the total number of people with ILIs presenting to the health-care system. This will help avoid straining health-care settings and so will decrease the likelihood of nosocomial transmission [i.e., infection that occurs in a hospital] of the COVID-19 or other emerging infectious agents in people under investigation for their disease.

Modelling the Benefits of Influenza Vaccination

We hypothesize that mass influenza vaccination prior to the onset of flu season could significantly reduce the number of ILIs in the general population, with fewer persons with ILIs requiring medical attention in high-risk settings. This would minimize the possibility of being late to identify circulating pathogens and so would reduce the likelihood of ongoing nosocomial transmission.

To test our hypothesis, we have devised a mathematical model focusing on the management of people with non-specific symptoms and complaining of ILIs, potentially at risk of developing the COVID-19 or other emerging respiratory infections during their investigation and work-up in the health-care setting[1].  This model framework was previously tested in a study to quantify impact of a preemptive mass influenza vaccination in controlling a SARS outbreak during a flu season. [2]

In our model, an important parameter is the total number of individuals with ILIs who subsequently seek medical advice in the outpatient or inpatient health-care setting. We examine a hypothetical situation where a COVID-19 or other respiratory pathogen outbreak coincides with the flu season. If there are still no reliable and rapid diagnostic tests, it will be extremely difficult to rapidly and accurately discriminate between the COVID-19 and other ILIs, given the non-specific clinical presentation of the COVID-19.

In a potential outbreak coinciding with the flu season, the combined total number of both flu and COVID-19 or other emerging respiratory pathogen patients seeking medical treatment in the same health-care settings will significantly increase. This, in turn, will lead to substantial increase of the probability of nosocomial transmission of the COVID-19. Therefore, we incorporate in our model a term describing a relationship between the vaccination coverage rate against influenza and the quarantine and isolation rate for the COVID-19. Doing so allows us to discuss qualitatively the impact of a mass influenza vaccination campaign on the containment of the COVID-19.

Our simulations and analyses indicate that the combination of an effective vaccination coverage rate against influenza and a moderate quarantine rate will be crucial in reducing the total number of people with ILIs. By increasing the influenza vaccination coverage rate to a particular threshold among the general population before the onset of the flu season, the need for a high and stringent quarantine and isolation rate to counteract the COVD-19 outbreak will be significantly reduced. This will save lives and spare valuable public health resources. Conversely, without reaching an effective influenza vaccination coverage rate, massive quarantine would be the only possible control strategy for the COVID-19 outbreak, even though it is practically unfeasible and unsustainable given its enormous costs and practical limitations.

Looking Forward – Flattening the Longer Curve

Based on the findings of our simulation, we call for an early launch of a  mass influenza immunization campaign to significantly improve the vaccine uptake rate and better control a hypothetical COVID-19 outbreak coinciding with the flu season. We note that seniors are particularly vulnerable to both the flu and COVID-19, hence an enhanced influenza vaccine program should also include high-dose vaccines to protect seniors and their caregivers.

Acknowledgment: The research has been funded by the CIHR 2019 Novel Coronavirus (COVID-19) rapid research program and by the Canada Research Chairs program.

References

[1] Q. Li, B. Tang, N. Bragazzi, J. Wu et al., The role of mass influenza vaccination in controlling a potential COVID-19 outbreak during the 2020-2021 flu season, preprint. http://www.liam.yorku.ca/index.php/liam-preprint/

[2]  Q. Zeng, K. Khan, J. Wu and H. Zhu, The utility of preemptive mass influenza vaccination in controlling a SARS outbreak during flu season, Mathematical Biosciences and Engineering, 4:4 (2007), 739-754.

Lironn Kraler, MD, and Kevin Schulman, MD, Stanford University School of Medicine

GPS Tracking Tools Will Help Track Social Distancing

Adherence to social distancing recommendations is crucial to limit the spread of COVID-19, but there are few tools to track this in real-time. Further, we currently lack specific tools to understand the behaviors within individual communities. Such knowledge can prompt local interventions for communities at risk for continued, unhindered spread of infection.

GPS tools embedded within smartphones can provide a unique resource to address the data needs for effective and meaningful social distance monitoring. Anonymized and aggregated location tracking may provide proof-of-concept that this technology can help to fight the COVID-19 epidemic. Analyses of these data may also provide support for more aggressive monitoring of population health using smartphones.

Google’s Tracking Tools

Google’s health research organization has developed a relevant set of applications. Google users who opt-in to share their location history are the source of common features such as live traffic information, as well as “Popular Times” for dining and recreational destinations on Google Maps. This same data source is now being used to populate a publicly available report, “COVID-19 Community Mobility Report” [ https://www.google.com/covid19/mobility/ ]

First published on April 3, 2020, the online resource shows changes in mobility over time for 131 countries. In the US, these data are available down to the state and county level. Mobility is measured by anonymized visits to high-level categories consisting of workplaces, transit stations, grocery stores, retail/recreation locations, and residential locations. Mobility changes are measured in comparison to a baseline of activity in that same region before social distancing was implemented.

Google’s Community Mobility Report is being offered to the public to inform local health officials in their response to the COVID-19 epidemic. The report illustrates, for example, that as of April 5, 2020, Santa Clara County, California experienced a 68% reduction in mobility related to “retail and recreation” establishments compared to baseline; at the same time, “residential” mobility increased 19%. The data are timely, with only a two to three day lag. Google has solicited feedback on this tool so that it can consider modifications to improve its usefulness for users.

Using the Data from the Tracking Tool

Correlating Social Distancing to Infection Control

While a threshold of clinically meaningful social distancing by smartphone location tracking is not known, it may be reasonably extrapolated based on contemporaneous reports of new positive COVID-19 diagnoses in the region. In Santa Clara County, where shelter-in-place orders took effect on March 11, the infection rate has remained stable, rather than the exponential growth that was originally projected.[1] Therefore, health officials of Santa Clara county might use the measures of mobility in this report to better estimate the relationship between the extent of social distancing and the concurrent spread of COVID-19 in this community. Conversely, in regions where infection rates continue to rise, such mobility reports can serve as a more concrete point of reference as public health efforts are intensified.

Assessing social distance relaxation

As local health officials around the globe begin to relax certain social distancing mandates, the data in these reports can help illustrate what is happening in terms of population movement. For example, if staggered return to work is permitted but the report suggests greater mobility than expected, these policies can be reassessed promptly. In addition, the exact patterns of mobility in relation to viral spread could provide critical insights compelling more tailored social distancing efforts specific to local conditions.

Potential for identifying crowding

Currently, the report illustrates mobility changes en bloc for destinations (e.g., “retail and recreation” or “grocery stores”). However, a more detailed mobility report could offer significant value. For example, in regions where socially distanced outdoor activities are still permitted, a more granular breakdown of mobility in public destinations would clarify where there remains unacceptable crowding within a region and consequently merit additional public health efforts. In contrast, if locations providing essential services are experiencing unacceptable crowding, this can inform officials to permit expanded hours of operation to lessen unnecessary clustering, or to facilitate other food or supply delivery mechanisms.

Individual data are not included, but more contact tracing is on the horizon

The current report does not provide data to track individual mobility, to identify social distancing scofflaws, or to permit contact tracing of infected individuals in order to digitally trace transmission routes to identify potentially exposed persons.  In the coming months, Google and Apple in a joint partnership will be releasing APIs to enable interoperable apps for opt-in contact tracing of infected persons in collaboration with public health authorities. [2]

Looking Forward

Google’s Community Mobility Reports have the potential to offer the crucial data needed to understand population movements, effectively monitor adherence to social distancing policies within communities, and better manage the spread of COVID-19. An opt-in service for tracking individuals, while acceptable for marketing and other convenience smartphone features, may be limited in this time of crisis. If these data prove useful in monitoring the epidemic, policy makers should consider whether anonymized mobility data should be collected through an opt-out basis during this time.

References

[1] https://www.sccgov.org/sites/covid19/Pages/home.aspx#cases

[2] https://www.apple.com/newsroom/2020/04/apple-and-google-partner-on-covid-19-contact-tracing-technology/

Kelly McFarlane, BS^1,2+, Teng Zhang, BA³⁺, Jacqueline Vallon, BS BA³⁺, Linying Yang, BS⁴, Jin Xie, BS⁴, Peter Glynn, PhD³, and David Scheinker, PhD^3,7-9

¹Stanford Graduate School of Business, Stanford, CA

²Harvard Medical School, Boston, MA

³Department of Management Science and Engineering, Stanford University School of Engineering, Stanford, CA

⁴Institute for Computational & Mathematical Engineering, Stanford University School of Engineering, Stanford, CA

⁷Clinical Excellence Research Center, Stanford University School of Medicine, Stanford, CA

⁸Department of Pediatrics, Stanford University School of Medicine, Stanford, CA

⁹Department of Perioperative Services, Lucile Packard Children’s Hospital, Stanford, CA

⁺These authors served as co-first authors

Hospitals Dealing with COVID-19 Need to Estimate Demand for Beds and Ventilators 

Hospital leaders need tools to help them prepare for a surge in COVID-19 patients in the face of limited testing capabilities as well as significant uncertainty about the rate at which COVID-19 is spreading. Intensive (ICU) and acute care (AC) beds as well as ventilators are critical for the care of COVID-19 patients. There have been severe shortages in hard-hit areas such as China, Italy, and New York.^1,2 As states and hospitals scramble to open IC beds by canceling elective surgeries and purchasing more ventilators, they need to know how many beds and ventilators they will need, and when, in the coming days and weeks.

Hospital Bed Demand Projections for Capacity Planning

To facilitate hospital capacity planning in the presence of this uncertainty, we built an accessible, interactive model to estimate the number of IC beds, AC beds, and ventilators necessary to accommodate COVID-19 patients who require hospitalization along with the non-COVID-19 patients in the hospital.³ This model has been published as an online tool which allows users to modify the basic input parameters in order to tailor the projections to their institution.⁴

Hospitals leaders can input data specific to their facilities, along with estimates of the characteristics of the patient population in their region. Inputs include the starting census of COVID-19 positive and negative patients at the hospital. The estimated doubling time for total COVID-19 admissions (i.e., how many days it will take for the number of total admissions at the institution to double) is an important input to which the model is sensitive. Estimates regarding the patient length of stay and trajectory through the hospital are also inputs generated through review of existing data and discussion with experts. These parameters help to calculate how many COVID-19 patients will be in the hospital at a given time and for how long.^5,6

In order to compare the projected demand to a hospital’s available resources, users can also input current capacity data including the number of IC beds, AC beds, and ventilators in the hospital in order to predict when capacity will be reached. Adjusting these capacity numbers allows the hospital to visualize the effect of different capacity expansion plans with regards to when capacity will be reached and exceeded.

An additional capability of the model is the ability to upload multiple days’ worth of census data. The model will display the current data in addition to the projections starting from the most recent date forward. This allows hospital managers to make constant adjustments to their forward projections based on what they are seeing in-house at any given time.

Value of the Model

The model is based on transparent logic and starts with simple, known inputs in addition to certain informed estimates in order to provide a robust basis of projections for hospital managers. The projections complement the many different forecast models being created for COVID-19 to help with various aspects of the uncertainties we are facing. For example, our colleagues have developed a model to calculate regional demand based on county-level hospitalization data.⁷ Our model similarly functions much like a calculator, helping hospitals determine their need for beds in the coming days and weeks with a manageable number of parameters and clear logic.⁸

The model highlights two aspects of the pandemic, concerning the rate of increase in cases and unbalanced demand for AC and IC beds. First, the estimates are particularly sensitive to the doubling time of new COVID-19 cases, emphasizing the importance of the doubling time in relation to the hospital’s ability to manage capacity; this supports the public health interventions focused on social distancing. Second, the model shows that AC beds may reach capacity before IC in certain scenarios. This is actionable information for managers who must develop and implement strategies to decrease occupancy in various parts of the hospital, such as cancelling elective procedures and accelerating efforts to cohort patients and discharge patients to appropriate step-down care facilities.

Hospitals need to address critical questions about staffing patterns and equipment capacity. This model can assist with decision-making in real-time as the epidemic unfolds.

Acknowledgements

We thank Systems Utilization Research for Stanford Medicine (https://surf.stanford.edu/) and the Clinical Excellence Research Center (http://med.stanford.edu/cerc.html) for their invaluable contributions.

Figure 1: Screen capture of tool (https://surf.stanford.edu/covid-19-tools/covid-19-hospital-projections/, accessed 04/03/2020)  with permission of authors. The plot is a hypothetical situation and not a forecast for any specific hospital.

Figure 2: Screen capture of tool showing inputs for starting census, patient cohorts, and length of stay estimates (https://surf.stanford.edu/covid-19-tools/covid-19-hospital-projections/, accessed 04/03/2020)  with permission of authors.

References

1. Wu Z, McGoogan JM. Characteristics of and Important Lessons from the Coronavirus Disease 2019 (COVID-19) Outbreak in China: Summary of a Report of 72 314 Cases From the Chinese Center for Disease Control and Prevention. JAMA. Published online February 24, 2020. doi:10.1001/jama.2020.2648
2. Grasselli G, Pesenti A, Cecconi M. Critical Care Utilization for the COVID-19 Outbreak in Lombardy, Italy: Early Experience and Forecast During an Emergency Response. JAMA. Published online March 13, 2020. doi:10.1001/jama.2020.4031
3. Zhang T, McFarlane K, Vallon J, Yang L, Xie J, Blanchet J, Glynn P, Staudenmayer K, Schulman K, Scheinker D. A model to estimate bed demand for COVID-19 related hospitalization. medRxiv. https://doi.org/10.1101/2020.03.24.20042762
4. SURF Stanford Medicine, Stanford University. COVID-19 Hospital ICU and Floor Census Model. https://surf.stanford.edu/covid-19-tools/covid-19-hospital-projections/ (Accessed April 2, 2020)
5. Zhou, Fei, et al. Clinical course and risk factors for mortality of adult inpatients with COVID-19 in Wuhan, China: a retrospective cohort study. The Lancet. Published online March 11, 2020. doi:https://doi.org/10.1016/S0140-6736(20)30566-3
6. Guan, Wei-jie, et al. Clinical characteristics of coronavirus disease 2019 in China. New England Journal of Medicine. Published online February 28, 2020. doi:10.1056/NEJMoa2002032
7. SURF Stanford Medicine, Stanford University. Projecting Severe Cases of COVID-19. https://surf.stanford.edu/covid-19/, last accessed April 3, 2020.
8. Shah, N. A calculator and a Model race to save a COVID-19 patient. Medium.com. https://medium.com/@nigam/a-calculator-and-a-model-race-to-save-a-covid19-patient-b26a40e43302, last accessed April 3, 2020.

Pinar Karaca-Mandic, PhD; Archelle Georgiou, MD; Soumya Sen, PhD; Yi Zhu; Khoa Vu; Alexander Everhart
University of Minnesota

Pandemics Are Inevitable – A Weak Response Is Not

While the emergence of COVID-19 may not have been preventable, the tragic impact of the virus could have been mitigated with better preparation and response. As the pandemic unfolds, one of the greatest barriers to prospective planning has been credible and consistent data. While case rates have been reported in the U.S. since January 20 – the date of the first confirmed case of 2019-nCoV infection in the country – this does not adequately reflect the spread of the virus because testing capability has been limited. As a result, the only metric available from all 50 states is the number of deaths, at least those that are not attributed to other causes, a moderately accurate but trailing indicator that does not provide an adequate lens to the impact of the crisis on our healthcare infrastructure.

A Data Collection and Reporting Dashboard for Hospital-Based Deaths

In March, the Medical Industry Leadership Institute [1]and the Management Information Systems Research Center [2] at the University of Minnesota Carlson School of Management launched a project to collect daily data from state Departments of Health on COVID-19 hospitalizations. The project team sent serial emails to the communication/media director of each state describing the project and requesting information starting March 26. At that time, only 23 states publicly reported any hospitalization data on their publicly available websites.

Fortunately, we now have a majority of states. As of April 10, 40 states are reporting data on hospitalizations: 23 states are reporting total hospitalizations to date and 21 states report current hospitalizations.  Minnesota, Montana, North Dakota, and Oregon report on both measures for COVID-19 hospitalizations.

Since April 6, this data has been shared on a publicly available dashboard.  The University of Minnesota COVID-19 Hospitalization Tracking Project (https://carlsonschool.umn.edu/mili-misrc-covid19-tracking-project) displays real-time and historical (since project inception) data for current and total hospitalizations as well as ICU data. All data are adjusted for states’ population. In addition, each state’s hospital bed capacity is calculated. The projects’ early results, reflecting nine days of data, were published in Health Affairs.

Actionable Insights From the Dashboard

Ongoing data collection expand these insights and, after only 16 days of data collection and tracking, there are new actionable insights from this new comprehensive state by state view. Two key implications involve variance among states and forecasts of hospital resource needs.

• High variance in hospitalization among states. The average total hospitalizations per 100K adults is 14.9 (Table 1) among the 23 reporting states. Minnesota’s rate (7.6) is significantly lower than the average and remarkably lower than Wisconsin (20.6), a neighboring state. Public health officials can use these data comparisons to identify and potentially adopt best practices from other states. In addition, visibility to the hospitalization data of neighboring states means states can work with each other to share resources – from hospital capacity to ventilators to personal protective equipment to personnel.

• Hospitalization trends are leading indicators of resource needs. Trending each state’s hospitalizations per 100K adults over time (Figures 1 and 2) offers a lens into the severity and activity of the virus. As the pandemic’s impact in Massachusetts, Georgia, Maryland, and Colorado trails one to two weeks behind New York and New Jersey, state health agencies can perform predictive modeling of their upcoming needs to forecast their hospital bed utilization. At a federal level, this national view can help optimally allocate the addition of hospital resources and capacity across states.

Figure 1: Total hospitalization to date per 100K adult population

Looking Forward

We will continue daily data collection for the foreseeable future. We are also collecting more within-state geography data that will refine modeling and support predictive analyses to help optimize management of hospital resources and capacity. This is particularly important as we anticipate and prepare for a potential second round surge of COVID-19 in the fall.

Pinar Karaca-Mandic, Ph.D. is Associate Professor, Department of Finance and Academic Director, Medical Industry Leadership Institute (MILI) Carlson School of Management, University of Minnesota. She is also a Research Associate at the National Bureau of Economic Research, Health Economics and Health Care programs

Archelle Georgiou, MD is Chief Health Officer, Starkey Hearing Technologies and Executive in Residence, Carlson School of Management, University of Minnesota

Soumya Sen, Ph.D. is Associate Professor, Department of Information & Decision Sciences, and Research Director of MIS Research Center, Carlson School of Management, University of Minnesota

Yi Zhu is a PhD student at the Carlson School of Management, Department of Information & Decision Sciences, University of Minnesota

Khoa Vu is a PhD student at the Applied Economics Department, University of Minnesota

Alexander Everhart is a PhD student at the Health Services Research, Policy, and Admin Program

We also acknowledge support from undergraduate research assistants Christopher Rose, Ansiya Khan, Lauryn MacLeod, Brooke Wiegert, from Kimberly Choyke, M.S.Ed, Medical Industry Leadership Institute Program Administrator as well as from the Carlson School of Management Information Technology Team.

References

[1] https://carlsonschool.umn.edu/faculty-research/medical-industry-leadership-institute

[2] http://www.misrc.umn.edu/