LII:COVID-19 Testing, Reporting, and Information Management in the Laboratory/Workflow and information management for COVID-19 (and other respiratory diseases)
- 1 3. Workflow and information management for COVID-19 (and other pandemics)
- 1.1 3.1 Laboratory informatics and workflow management
- 1.1.1 3.1.1 Does the system provide a flexible provider portal?
- 1.1.2 3.1.2 Does the system allow for the flexible addition of users, providers, and patients?
- 1.1.3 3.1.3 Does the system allow for laboratory workflows sympathetic to COVID-19 and other types of respiratory illness testing?
- 1.1.4 3.1.4 Does the system allow for interfacing with most instruments currently used for testing for COVID-19?
- 1.1.5 3.1.5 Does the system allow for versatile viewing and reporting of COVID-19 results?
- 1.2 3.2 Laboratory informatics and reporting requirements
- 1.3 3.3 Additional benefits and challenges of laboratory informatics in disease testing and public health
- 1.1 3.1 Laboratory informatics and workflow management
- 2 References
- 3 Citation information for this chapter
3. Workflow and information management for COVID-19 (and other pandemics)
3.1 Laboratory informatics and workflow management These benefits are achieved through automation elements that reduce data entry errors, reduce workloads, collect laboratory instrument data, and check for common errors like duplicate test orders. In the world of epidemiological testing, those same laboratory informatics applications—such as laboratory information management systems (LIMS), laboratory information systems (LIS), and hospital information systems (HIS)—provide similar value.
Pandemic response realizes benefits through crisis and risk management systems, syndromic surveillance systems, and medical diagnostic tools. As Norwegian researchers Wilson and Jumbert note about humanitarian technologies and pandemics, "collecting information is central to the implementation of an efficient response, including situational information, needs assessment, and operational information." At the response's core is the valuable reporting of public health data (discussed in the next section). As such, those labs and healthcare systems performing disease testing see numerous benefits in adopting and applying informatics solutions to their workflow: improved operations and positive contributions to disease reporting.
However, just purchasing a random laboratory informatics solution and putting it to use is no guarantee towards realizing the technology's actual benefits. Careful consideration, discussion, training, and policy adjustment are required to get the most of any new system. It would be beyond the scope of this guide to offer complete advice on acquiring and implementing laboratory informatics solutions. That information can be found in the Association of Public Health Laboratories' Laboratory Information Systems Project Management: A Guidebook for International Implementations or Joe Liscouski's A Guide for Management: Successfully Applying Laboratory Systems to Your Organization's Work. What follows instead are considerations to make when selecting a solution to assist your organization with COVID-19 (and other types of disease) testing workflows.
3.1.1 Does the system provide a flexible provider portal?
What types of providers are ordering COVID-19 tests? From surgeons ordering for pre-operation procedures and emergency room physicians for ER patients, to pathology groups and home health care or assisted living centers ordering for their patients, a wide variety of provider types exist. Those provider types and their special needs should be addressed. For example, physicians of record for home health care clients may not only require support for digital signatures in order entry, but additional verbal authorization of the test may also be required. The system should have a means for verifying that these order entry components are entered by the provider.
Healthcare facilities may also require additional flexibility for portal account creation and use. For example, they may find it useful to have portal log-ins tied to a facility rather than a specific physician. An entity may even wish to provide in-house staff or other related workers access to their COVID-19 test report via the portal, requiring role-based permissions to be built into the portal. Be sure to consider who needs access to what information and whether or not the LIMS or LIS can securely meet those needs.
Some healthcare systems will require the order entry portion of the provider portal make disease-specific checks or require disease-specific patient symptoms to be entered as part of the order. In the case of COVID-19, a wide majority of healthcare settings are still requiring the patient to be indicating clinical and/or epidemiological evidence of SARS-CoV-2 infection before testing may begin. Does the system provide checks for testing requirements or, at a minimum, allow documentation of patient aspects such as body temperature, symptoms, travel history, and existing health conditions as part of order entry? This may be implemented through something as simple as comment boxes or through a more refined form with checkboxes and other input areas.
3.1.2 Does the system allow for the flexible addition of users, providers, and patients?
Providers ordering COVID-19 tests may work at more than one facility, ordering tests at a hospital one day, and ordering tests as a physician of record for a patient in a home health care setting the next. Having one system account for the provider while maintaining the ability to select the location associated with a test order is incredibly useful. In addition, being able to view test orders and reports by location has utility. As such, the system should not only make it easy to add providers and other users, but also allow the assignment of locations to those providers. Of course, the system should also allow for more granular assignments of system roles to users. The system should also allow patients to be added to the system as entities.
3.1.3 Does the system allow for laboratory workflows sympathetic to COVID-19 and other types of respiratory illness testing?
Sample reception should support single-sample orders as well as sample lots. The system should also allow for multiple sample types to be added. For COVID-19, this has typically involved nasopharyngeal swabs in a sterile viral transport container. However, other sample types such as sputum, blood, or saliva—and other container types such as a sterile container with saline, a sterile dry collection cup, or blood collection tubes—should also be supported. Sometimes samples won't be available at the same time the test order arrives because sampling needs to be scheduled. In the case of COVID-19 testing, priority is being given to healthcare workers and patients showing clinical evidence of SARS-CoV-2 infection; however, as testing becomes more readily available, workplace and student testing may be phased in, for example. This requires scheduling of patients to provide samples in intervals of time. Does the informatics system provide a means for providers and laboratory personnel to schedule sample collection associated with test orders? Can it send appointment reminders to scheduled patients, and can it send alerts if the patient doesn't arrive, completes sampling procedures, or views their patient results?
Order and sample management
Viewing and managing test orders specific to COVID-19 and other illnesses should be painless. The system should make it clear in what workflow step a requested test sample is located, from received but not processed, to in-analysis or requiring results approval. If tests for multiple diseases are ordered, the system should allow users to filter tests and related samples by specific test type, such as "SARS-CoV-2 rRT-PCR," or by test result (e.g., "Negative" or "Positive") or testing location (e.g., molecular pathology, serology, "Lab 2-B"). The system should also have the flexibility to show which analyst or instrument is assigned.
Workflow or "batch" management
Laboratories have their own workflows, and the informatics system they use should be flexible enough to allow users to manage the various steps or "batches" in the workflow. The lab may require a few simple preparation and analysis steps, or it may require a more complex, specific set of steps. This requires system functionality that can readily support the workflow. For example, can specific instruments be assigned to a workflow step? Can the system automatically add quality control (QC) or duplicate samples to a step? Can they be added manually? Despite slightly relaxed quality control frequencies by professional groups such as the College of American Pathologists during the COVID-19 pandemic, they still require quality control tests as described on COVID-19 test kit package inserts.
- Prepare the batching for the ordered rRT-PCR tests, including who is involved, when it is scheduled, and any additional unique identifiers.
- Extract, purify, and assess the quality of nucleic acids from a complex biological sample.
- Prepare and assemble rRT-PCR components (including reverse transcriptase enzyme, primers, and nucleotides) and reactions to plates or tubes.
- Run the analysis using the appropriate quantification method.
- Review and take action on the analytical results.
Ensure the laboratory informatics solution developer can explain how that workflow can be further optimized and tracked within the informatics solution.
As orders move through the various steps of a lab's workflow, approval processes may be required. With COVID-19 diagnoses in particular, taking appropriate steps to limit the number of false-negative test results is vital, requiring careful results review and approval processes. Laboratory analysts may approve the samples through the initial workflow steps, or those steps may be automated. Eventually, however, the analytical steps are completed, and results ready for review.
Depending on the tests being run, the initial default value for a test that hasn't been run should be configurable in the system, either as a "negative" result or an empty or null value. Upon completion of the analysis, the system then should make it abundantly clear which samples in a batch are within and out of test limits, as well as sufficiently easy to manage approval of results. Ideally, tested samples that are within limits will still show the initial default value or, if the initial default was null, show an appropriate value such as "negative." Results that are out of limit should not only show a "positive" or other appropriate result state, but also color coding, flags, or other visual cues that make the outlier status of the sample clear to the analyst.
Finally, can the system handle reflex testing automatically when results are produced? For example, the lab may want a presumptive positive for COVID-19 to trigger the system to automatically add confirmation tests to the test queue for the associated patient. The correct people will also need to be notified of such reflex test creation in the system.
3.1.4 Does the system allow for interfacing with most instruments currently used for testing for COVID-19?
Instrument interfaces (discussed later in this chapter) are typically a non-trivial monetary investment for laboratories as the developer has to take into account compliance requirements, the required analytical outcomes, and the instrument itself, as well as its available connections. Yet a laboratory informatics vendor with experience has likely already set up interfaces to the instruments used in testing COVID-19 and other respiratory illnesses. RT-PCR systems like Hologic's Panther Fusion and Abbot's RealTime m2000 represent a few of the systems being used to test for COVID-19 right now. Is the laboratory informatics system able to interface with these and other lab-based and point-of-care (POC) instruments you may require, at a reasonable cost? If so, also ensure the ease of assigning those instruments to specific tests or samples in the system. You should also be able to document well numbers for the analysis, as well as later view which samples are associated with a particular instrument. (In some cases, an instrument may be solely allocated to one specific test type.)
3.1.5 Does the system allow for versatile viewing and reporting of COVID-19 results?
Already mentioned was the process of results approval and the importance of being able to clearly view those results both within and out of test limits. Of course, this information has to be reported for internal and external purposes. Internally, the laboratory or healthcare entity using the informatics system will want to understand their test volume and associated details. Additionally, a laboratory results report for SARS-CoV-2 infection will also have to be distributed to the local or state health department along with a case report. Externally, ordering physicians and other external customers—including patients—will require clear and timely results in the form of a report. How does the laboratory informatics solution help with these and other reporting requirements? Can it assist with any external electronic reporting requirements (such as those with the Centers for Medicare & Medicaid Services) you may have?
The system's reporting tools should be configurable to the lab's needs. The reporting itself should provide fields for the addition of comments and consultation notes from the lab and the physician, as the stakeholders need to give clinical and diagnostic guidance based on not only the results but also critical comments. In some cases, a lab or healthcare system may require the addition of comments at different stages of reporting. Perhaps an initial results report is created by the lab and sent to the provider via the provider portal, but a pathologist needs to review the report and add additional commentary concerning future treatment or concerns about the test results. This requires the system be flexible enough to allow additional steps before a report is finalized.
Finally, some solutions may include a disease- or test-specific dashboard that can show all samples related to a test, any positives, any negatives, pending results, tests per day, etc. Having this information available in one location can help facilitate reporting to government entities (e.g., reporting statistics to FEMA during emergencies While not strictly necessary, ensure the solution can fulfill your needs with displaying real-time and near-time information to better support rapid decision making.
3.2 Laboratory informatics and reporting requirements
Epidemiology can broadly be split into two categories: descriptive epidemiology and analytical epidemiology. Descriptive epidemiology involves studies and other activities that deal with geographical comparisons and temporal trend descriptions of disease. As such, the collection and use of quality incidence data is vital to developing hypotheses. Analytical epidemiology allows for the testing of those hypotheses using both experimental and observational studies, as well as control groups. Similarly, the collection and use of quality experimental and observational data is vital for proving or disproving hypotheses. In both cases, proper reporting of public health data is critical to the success of epidemiologists' response to outbreaks and pandemics, as well as the credibility of their research.
The proper reporting of COVID-19 case data is no exception. In the United States, the Centers for Disease Control and Prevention (CDC) has taken a standardized approach to collecting reports on "individuals with at least one respiratory specimen that tested positive for the virus that causes COVID-19." Their COVID-19 Case Report Form is designed to collect a wide variety of information about a COVID-19 case, including patient demographics, epidemiological characteristics, exposure and contact history, and clinical diagnosis and treatment procedures. Currently, the CDC is asking local and state health departments to submit case reports, and asking healthcare providers and laboratories to contact those health departments when "concerned that a patient may have COVID-19." The CDC has also slimmed its reporting requirements, limiting reporting of "persons under investigation" to areas where testing must be forwarded to the CDC due to insufficient capacity to test locally. Electronic reporting using the CDC's system is preferred, but they have a protocol for those areas unable to submit electronically. Canada has similar reporting expectations, with their own case report form and electronic data submission process through the Public Health Agency of Canada. And in the European Union, member countries and the U.K. are asked to report through the Early Warning and Response System.
3.2.1 ICD and CPT codingInternational Statistical Classification of Diseases and Related Health Problems (ICD) is a commonly used system of diagnostic codes for classifying diseases, including nuanced classifications of a wide variety of signs, symptoms, abnormal findings, complaints, social circumstances, and external causes of injury or disease. Their ICD-10-CM code set has been modified to include lab testing codes for COVID-19, as has the Current Procedural Terminology (CPT) code set. This guide provides basic information about these codes (which should not be considered legally binding advice); however, see the referenced material (and following citations) for more details concerning those codes:
- The CPT code 87635 has the long descriptor of "Infectious agent detection by nucleic acid (DNA or RNA); severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) (Coronavirus disease [COVID-19]), amplified probe technique." The American Medical Association's CPT Assistant fact sheet for SARS-CoV-2 reporting proves useful in supplying assistance on how and when to apply this CPT code in reporting.
- The U.S. government-adopted ICD-10-CM—an authorized version of WHO's ICD-10—has been updated by the CDC and the National Center for Healthcare Statistics. It includes not only new codes for conditions associated with COVID-19 but also codes for exposure and screening. The principal diagnosis code "U07.1, COVID-19" is sequenced first, followed by any appropriate codes for associated manifestations (though there is an obstetrics exception). The CDC's official coding guidelines, as well as guidance from Dr. Erica Remer may prove useful in choosing the correct codes.
Laboratories analyzing specimens for SARS-CoV-2 must be equipped to implement and handle analytical testing and test orders using the new test codes. However, they also must be able to quickly and accurately transfer vital case information to the appropriate health authority.
3.2.2 Reporting to local and regional health departments
Given the valuable nature of case reports during an epidemic, health care providers, facilities, and laboratories are being held responsible for sending case data to their local and regional health departments. That information then feeds up to the state-level health department, which then makes its way to the national-level entity responsible for handling epidemiology (in the case of the U.S., the CDC). Significant decisions are typically based on that data. For example, the U.S. CDC has been conducting seroprevalance (determination of the "percentages of people who were previously infected with SARS-CoV-2") surveys of various regions of the United States. The insights and decisions the CDC makes depends on cooperation and proper reporting by state and local health departments.
However, acquiring standardized data at the national level can be challenging. In the case of the U.S., general disease reporting requirements vary from state to state, with some states encouraging full electronic laboratory reporting (ELR), while others still encouraging faxed or mailed reports. Add in the urgency and confusion associated with a pandemic, and COVID-19 reporting requirements prove to vary just as much. Some states' health departments have taken a proactive approach to reporting. For example, Iowa's Department of Public Health has issued several mandatory COVID-19 reporting orders meant to supplement existing reporting rules, including an order requiring all Iowa health care providers and public, private, and hospital laboratories "to immediately report all positive and negative Coronavirus Disease 2019 (COVID-19) testing results to the department." Other states have not been as clear on their reporting requirements, in some cases not having any guidance documents or clear information on their health department website for how providers, facilities, and labs should report COVID-19. In those cases, the presumption is that most labs have contacted the health department for advice or are reporting COVID-19 cases as immediately reportable, based upon the state's existing reporting requirements for immediately reportable diseases.
Table 1 addresses the reporting requirements for the United States' 50 states, while Table 2 covers U.S. territories. If clear reporting guidance specific to COVID-19 could be found, it was described. If no such guidance could be found, then the state's existing guidance and rules regarding disease reporting were referenced. In some cases, the state health departments don't clearly spell out whether a faxed or mailed report is required after immediately phoning in a report. In other cases, it's not clear if ELR—though it exists—is an acceptable form of reporting COVID-19 cases. This ambiguity is stated in the form of a "(?)" found next to the "Y" and "N" for electronic filing and faxing. In all cases, if there is any doubt about reporting requirements, call your local health department to confirm.
3.3 Additional benefits and challenges of laboratory informatics in disease testing and public health
COVID-19 is at the forefront of the consciousness of humanity, by and large, and the informatics tools we implement for managing, treating, and surveilling the disease are of great import. From disease databases to electronic health records, from bioinformatics tools for peptide and protein modeling to laboratory tools such as LIMS and LIS, we continue to fight back against the threat of the SARS-CoV-2 virus. Yet despite the gravity of the pandemic, this is neither the first nor the last time laboratory and scientific informatics will play a positive role in testing for disease and improving public health outcomes.
Health informatics technology, when used responsibly, has already proven to be useful in studying and treating contagious diseases. In a 2013 research paper published in the journal BMJ Quality & Safety, El-Kareh et al. analyzed and described the state of diagnostic health information technology (HIT). They noted that without the aid of HIT, clinicians are more error-prone, leaving them "vulnerable to fallible human memory, variable disease presentation, clinical processes plagued by communication lapses, and a series of well-documented ‘heuristics,’ biases, and disease-specific pitfalls." Appropriate, well-designed HIT systems are capable of helping clinicians and laboratorians by providing more timely access to information, improved communication, better clinical reasoning and decision making, and improved workflows, as well as a reduction in diagnostic errors, and, as a result, improved patient safety and health outcomes.
From a public health perspective, the application of informatics to disease surveillance, reporting, and health habit promotion is also vital. Winters-Miner et al. note in particular the value of using informatics tools and methods to implement predictive analytics and data mining into public health. They use disease prevention and biosurveillance as major examples. We could, for example "analyze large populations of people to quantify risks related to public health, and help physicians to develop intervention programs for those patients at highest risk of some ailment or medical condition." Additionally, through the use of syndromic surveillance systems (tools aiding in the detection of indicators leading up to disease diagnosis for individuals and populations), they suggest that outbreaks can be better detected at local and national levels, and public health measures can be better implemented, increasing public awareness and hindering the spread of disease.
In the clinical laboratory, informatics systems have been influencing workflow improvements and improved service delivery for more than five decades. And while improvements have been seen in the laboratory from not only the introduction of computerized systems but also the realization of quality control and point-of-care testing, more challenges remain. For example, quality management in the laboratory is still often a manual, time-consuming activity. While the LIMS and LIS have some tools to assist with this task, the inclusion of laboratory analytics and business intelligence tools into those systems may lead to even further improvements in quality and efficiency in the lab. And in the realm of point-of-care testing, oversight and control of instruments can be lost when connectivity and training is lacking. Proper interfacing of these lab instruments could lead to improvements in those areas, says Siemens Healthineers' Daniel Gundler. "Maintaining POC instruments and overseeing the operators performing POC tests would be much easier if all the information and data from each instrument were accessible through one user interface in which coordinators could manage both the instruments and operators."
3.3.1 System interoperability Improving interoperability among clinical informatics systems is recognized as an important step towards improving health outcomes. The National Academies of Sciences, Engineering, and Medicine had much to say on this topic in their 2015 publication Improving Diagnosis in Health Care:
Improved interoperability across different health care organizations—as well as across laboratory and radiology information systems—is critical to improving the diagnostic process. Challenges to interoperability include the inconsistent and slow adoption of standards, particularly among organizations that are not subject to EHR certification programs, as well as a lack of incentives, including a business model that generates revenue for health IT vendors via fees associated with transmitting and receiving data.
In particular, they discuss an additional concern, one that still causes issues today: interfaces between electronic health records (EHR) and the laboratory and other clinical information systems that feed medical diagnostic information into the EHRs:
Additionally, the interface between EHRs and laboratory and radiology information systems typically has limited clinical information, and the lack of sufficiently detailed information makes it difficult for a pathologist or radiologist to determine the proper context for interpreting findings or to decide whether diagnostic testing is appropriate. For example, one study found that important non-oncological conditions (such as Crohn’s disease, human immunodeficiency virus, and diabetes) were not mentioned in 59 percent of radiology orders and the presence of cancer was not mentioned in 8 percent of orders, demonstrating that the complete patient context is not getting received. Insufficient clinical information can be problematic as radiologists and pathologists often use this information to inform their interpretations of diagnostic testing results and suggestions for next steps. In addition, the Centers for Disease Control and Prevention’s Clinical Laboratory Improvement Advisory Committee (CLIAC) expressed concern over the patient safety risks regarding the interoperability of laboratory data and display discrepancies in EHRs. They recommended that laboratory health care professionals collaborate with other stakeholders to “develop effective solutions to reduce identified patient safety risks in and improve the safety of EHR systems” regarding laboratory data.
In fact, interoperability issues have come up during the global laboratory response to the COVID-19 pandemic. In early April 2020, a report from Nature revealed that academic research laboratories wanting to assist with COVID-19 testing efforts have at times been stymied by the incompatibility between academic informatics systems and hospital EHRs. Not only do hospitals use EHRs of differing types, but many of those EHRs were not designed to talk to other EHRs, let alone to academic and research laboratories' informatics systems. Combine this with strict account procedures and the costs of developing interfaces on-the-fly, more than a few medical systems have turned away the offer of help from academic and research labs. As it turns out, HL7- and other standard-based interfaces have long been expensive for many vendors to implement, the cost justified typically when high volumes of samples are involved. Additionally, in more normal, non-pandemic circumstances, the requirement to interface with EHRs and hospital information systems (HIS) is almost exclusively found in the LIS and LIMS used in patient settings, i.e., in the hospitals, medical offices, and laboratories catering to diagnosing disease in patients. Academic labs have not been equipped at any level (software, hardware, or personnel) to do high volume clinical testing, nor have they had reason to ensure their informatics systems can interface with clinical systems.
Interoperability benefits and challenges show up elsewhere too. Take for example the value of phenotypes, a representation of the genetic analysis of the collective observable traits of an organism, traits caused by the interaction of its genome with the environment. The value of patient phenotyping data is increasingly useful in the fight against known and novel viruses, as well as a broad variety of non-viral diseases. As Ausiello and Shaw note, in order for medicine to advance and produce improved patient outcomes, "traditional clinical information must be combined with genetic data and non-traditional phenotypes and analyzed in a manner that yields actionable insights into disease diagnosis, prevention, or treatment." Whether it's identifying "the measurable phenotypic characteristics of patients that are most predictive of individual variation" in treatment outcomes for chronic pain or COVID-19, phenotypes have utility in the clinical sector.
Here again interoperability between EHRs and laboratory informatics systems comes into play. In a 2019 paper published by Zhang et al. in nph Digital Medicine, the topic of extracting patient phenotypes from laboratory test results fed into EHRs is addressed. The authors state that one of the more difficult aspects of their research is that while "[l]aboratory tests have broad applicability for translational research ... EHR-based research using laboratory data have been challenging because of their diversity and the lack of standardization of reporting laboratory test results." They add:
Despite the great potential of EHR data, patient phenotyping from EHRs is still challenging because the phenotype information is distributed in many EHR locations (laboratories, notes, problem lists, imaging data, etc.) and since EHRs have vastly different structures across sites. This lack of integration represents a substantial barrier to widespread use of EHR data in translational research.
The answer to the clinical and laboratory interoperability question is unclear. A 2019 article in the American Association for Clinical Chemistry's CLN Stat addressed remaining roadblocks, including lack of standards development, data quality issues, clinical data matching, lack of incentivizing health IT optimization, text-based reporting formats, differences in terminology, and HL7 messaging issues. They add that proposals from the Office of the National Coordinator for Health Information Technology (ONC) and the Centers for Medicare and Medicaid Services include possible fixes such as standardized application programming interfaces (API). They also note that middleware may pick up the slack in connecting more laboratory devices, rather than depending on the LIS to handle all the interfacing.
Even so, it remains obvious that more work needs to be done in the development and standard use of clinical and laboratory informatics applications if the promise of personalized medicine and the need for improved disease testing and response are to be fulfilled. In particular, how we responsibly protect personal health information while putting its anonymized variants to beneficial use for disease testing and prevention remains a critical question that must be solved in order to better prepare for the next COVID-19.
3.3.2 Contact tracing The seeds of contact tracing may go as far back as late fifteenth and early sixteenth century efforts to control infection rates of syphilis among prostitutes in part of Europe. However, the practice was adopted more vigorously in the late nineteenth and early twentieth century in Great Britain for tracking cases of communicable diseases such as measles and sexually transmitted diseases like syphilis and gonorrhea. Further inroads were made in the United States in the early 1930 with its efforts to reduce sexually transmitted diseases among U.S. troops.
Throughout it all, contract tracing has been predominately a manual effort. However, the technological age has brought with it both social promise and privacy questions in regards to the application of informatics to contact tracing. In the mid-2000s, the use of Wi-Fi and RFID technology in Singapore hospital settings "to provide an alternative to tedious and error-prone manual contact tracing" was being reported in academic literature. Since then, the use of informatics tools in contact tracing has increased, seeing practical application in combating severe acute respiratory syndrome (SARS), Ebola, and tuberculosis. And now COVID-19 is being discussed within the context of both manual and digital contract tracing efforts.
Contact tracing using digital tools comes with benefits and challenges. Digital contact tracing tools have the potential to make contract tracing quicker and more efficient than manual processes. People can forget who they may have been in close contact with, for example, but with digital tracing, past location data can be mined to provide a clearer picture. As a result, with more accurate data, local and state policy-making efforts can be more nimbly tailored to current needs, and the effectiveness of those efforts can be better monitored. However, several challenges also exist, primarily with social and cultural perceptions of the technology's acceptability and the privacy concerns surrounding it.
The social acceptability and trust put into contact tracing applications and the governments that use them is of significant concern. A recent "blitz" survey of the Flemish Region of Belgium showed that only 51 percent of respondents had "no objection to a CORONA-app, under strict conditions." As legal researcher Domenico Orlando points out, many contact tracing applications like Italy's Immuni app, however, require at least 60 percent of the population to participate for it to be effective. He adds that while whether or not "trust and social acceptance will follow is an unknown variable" when it comes to digital contact tracing, having a strong set of legal protections in place—such as those found with E.U. data protection law and the efforts of the European Data Protection Board (EDPB) to create data protection guidelines for digital surveillance tools—at least makes the technology more feasible. Political and cultural context also plays a role in social acceptance. Countries like South Korea and states like Taiwan—in comparison to mainland China—arguably have seen greater adoption of digital surveillance due to a more relaxed political context, though attempts by the citizenry to be vigilantes who shame and discriminate others based on contact tracing data may work counter to that overall adoption rate. Recent past experiences with digitally tracing MERS and SARS outbreaks in both cases, however, have probably played an even greater role in the success of convincing those governments' citizenry to voluntarily participate. Similar adoption success can be seen in a Singapore poll that showed 70 percent of respondents were open to installing its government's voluntary TraceTogether app, which is based on the privacy-preserving protocol BlueTrace.
The preservation of user privacy and sensitive identifying data are also vital considerations in digital tracing apps. The previously mentioned BlueTrace protocol and its open-source reference implementation OpenTrace strive to address most of those concerns when "logging Bluetooth encounters between participating devices to facilitate contact tracing, while protecting the users’ personal data and privacy." More specifically, it strives to limit collection of personally-identifiable information, locally store and lock down encounter history, prevent third-party tracking, and provide revocable consent to store and allow use of encounter data. Ultimately, these and other such solutions must ensure the anonymity of tracing data doesn't become compromised. Lew and Anderson also note that "[c]loud-based storage of anonymous identifier beacons may also threaten security, given that any centralized list of identifiers could theoretically be hacked and re-identified." As noted previously, re-identified data, let alone leaked anonymized data, could lead to shaming and discrimination against individuals identified as being infected.
Privacy is not solely a concern of application developers, however. Government entities should also be held responsible for better ensuring how informatics solutions are created and used for epidemiological tracking. The European Union's EDPB and its contact tracing guidelines, adopted on April 21, offer a compromise between the societal needs of contact tracing and the individual needs of privacy. The EDPB addresses the topics of location data sources and anonymized use, as well as the recommendations and functional requirements of applications employing those data sources, concluding "that one should not have to choose between an efficient response to the current crisis and the protection of our fundamental rights: we can achieve both." The U.S. CDC is less eloquent with its COVID-19 digital contact tracing guidelines, though they still stress the preference for secure data transfer mechanisms, open-source architecture, need-to-know-only access to data for public health authorities, and the ability for users to revoke data access consent at any time.
Finally, most digital tracing solutions also suffer from a few additional downsides. Several researchers have expressed concerns about data accuracy and actionability issues in mobile devices that lead to false positives. These issues include:
- inaccurate inter-device distance detection
- inaccurate exposure detection in high-density buildings with multiple walls
- inability to track duration of exposure (i.e., seconds or hours)
- failures with the underlying technology itself, including BlueTooth or the operating system
Despite these and other drawbacks, various governments around the world have found greater success at managing the COVID-19 pandemic with digital contact tracing. Huang et al. in the Harvard Business Review wonder if the successes reported in South Korea, Taiwan and other parts of the East could be replicated in the United States and other Western democracies:
Can Western democracies achieve the results seen in East Asia without emulating their means? Probably not. There is likely a fundamental conflict between these requirements and deeply entrenched Western liberal values, such as the expectation of privacy, consent, and the sanctity of individual rights ... At the time of publication, at least three local governments in the United States are considering adoption of a contact-tracing app developed in a project led by MIT, Reuters reports ... But for such technologies to be effective, compliance must be nearly universal. Without a government mandate in the U.S., it’s hard to imagine universal voluntary adoption of even a privacy-protecting tracing app.
Maybe Covid-19 is a sign of our future steady state. Different societies will make different choices about how to respond to the next pandemic. For Western democracies the time has come to either rethink our values around the tradeoff between personal privacy and public safety in a pandemic or to accelerate technology innovation and policy development that can preserve both.
- Jones, R.G.; Johnson, O.A.; Baststone, G. (2014). "Informatics and the Clinical Laboratory". The Clinical Biochemist Reviews 35 (3): 177–192. PMC PMC4204239. PMID 25336763. http://www.pubmedcentral.nih.gov/articlerender.fcgi?tool=pmcentrez&artid=PMC4204239.
- Pitkus, A. (2018). "Laboratory Informatics: An Increasingly Valuable Commodity Emerging from Today's Laboratories". ASCLS Today 32 (2). https://www.ascls.org/communication/ascls-today/313-ascls-today-volume-32-number-2?start=10. Retrieved 30 April 2020.
- Raeen, M.R. (2018). "How laboratory informatics has impacted healthcare overall". Applied Research Projects 54. doi:10.21007/chp.hiim.0056. https://dc.uthsc.edu/hiimappliedresearch/54.
- Wilson, C.; Jumbert, M.G. (2018). "The new informatics of pandemic response: humanitarian technology, efficiency, and the subtle retreat of national agency". Journal of International Humanitarian Action 3: 8. doi:10.1186/s41018-018-0036-5. PMC PMC7149122. http://www.pubmedcentral.nih.gov/articlerender.fcgi?tool=pmcentrez&artid=PMC7149122.
- College of American Pathologists (07 April 2020). "Guidance for COVID-19 Testing for CAP-Accredited Laboratories". College of American Pathologists. https://www.cap.org/laboratory-improvement/news-and-updates/guidance-for-covid-19-testing-for-cap-accredited-laboratories. Retrieved 30 April 2020.
- Life Technologies Corporation (August 2012). "Real-time PCR handbook" (PDF). Life Technologies Corporation. https://www.gene-quantification.de/real-time-pcr-handbook-life-technologies-update-flr.pdf. Retrieved 30 April 2020.
- Starita, L. (01 April 2020). "COVID-19 SCAN molecular workflow". protocols.io. https://www.protocols.io/view/covid-19-scan-molecular-workflow-bebkjakw. Retrieved 30 April 2020.
- Udugama, B.; Kadhiresan, P.; Kozlowski, H.N. et al. (2020). "Diagnosing COVID-19: The Disease and Tools for Detection". ACS Nano 14 (4): 3822–3835. doi:10.1021/acsnano.0c02624. PMC PMC7144809. PMID 32223179. http://www.pubmedcentral.nih.gov/articlerender.fcgi?tool=pmcentrez&artid=PMC7144809.
- Pendergraph, G.E. (2020). "Reverse Transcriptase PCR (RT-PCR)". HIV: Structure, Replication, and Detection. https://www.labce.com/spg605456_reverse_transcriptase_pcr_rt_pcr.aspx. Retrieved 20 April 2020.
- Prinzi, A. (27 April 2020). "False Negatives and Reinfections: the Challenges of SARS-CoV-2 RT-PCR Testing". American Society for Microbiology. https://asm.org/Articles/2020/April/False-Negatives-and-Reinfections-the-Challenges-of. Retrieved 30 April 2020.
- John3504 (07 December 2011). "HL7 Interface cost and maintenance". Spiceworks. https://community.spiceworks.com/topic/175107-hl7-interface-cost-and-maintenance. Retrieved 25 April 2020.
- Strauss, D. (08 July 2018). "Connecting Lab Instruments: Interface Strategies Depend Upon Compliance Requirements". Lab Manager. https://www.labmanager.com/laboratory-technology/connecting-lab-instruments-interface-strategies-depend-upon-compliance-requirements-2034. Retrieved 30 April 2020.
- Hologic (17 March 2020). "Hologic’s Molecular Test for the Novel Coronavirus, SARS-CoV-2, Receives FDA Emergency Use Authorization". Hologic, Inc. https://www.hologic.com/coronavirus-test. Retrieved 30 April 2020.
- Abbott. "Abbott RealTime SARS-CoV-2 Assay". Abbott Laboratories. https://www.molecular.abbott/us/en/products/infectious-disease/RealTime-SARS-CoV-2-Assay. Retrieved 30 April 2020.
- Azar, A.M. (10 April 2020). "Coronavirus (COVID-19) Pandemic: HHS Letter to Hospital Administrators". FEMA. https://www.fema.gov/news-release/2020/04/10/coronavirus-covid-19-pandemic-hhs-letter-hospital-administrators. Retrieved 30 April 2020.
- Naito, M. (2014). "Utilization and application of public health data in descriptive epidemiology". Journal of Epidemiology 24 (6): 435–6. doi:10.2188/jea.je20140182. PMC PMC4213216. PMID 25327184. http://www.pubmedcentral.nih.gov/articlerender.fcgi?tool=pmcentrez&artid=PMC4213216.
- Centers for Disease Control and Prevention (2012) (PDF). Principles of Epidemiology in Public Health Practice (3rd ed.). Centers for Disease Control and Prevention. https://www.cdc.gov/csels/dsepd/ss1978/SS1978.pdf. Retrieved 11 April 2020.
- Hamilton, J.J.; Hopkins, R.S. (2019). "Chapter 5: Using Technologies for Data Collection and Management". In Rasmussen, S.A.; Goodman, R.A.. The CDC Field Epidemiology Manual (4th ed.). Oxford University Press. pp. 71–104. ISBN 9780190933692.
- von Elm, E.; Altman, D.G.; Egger, M. et al. (2007). "The Strengthening the Reporting of Observational Studies in Epidemiology (STROBE) statement: Guidelines for reporting observational studies". PLoS Medicine 4 (10): e296. doi:10.1371/journal.pmed.0040296. PMC PMC2020495. PMID 17941714. http://www.pubmedcentral.nih.gov/articlerender.fcgi?tool=pmcentrez&artid=PMC2020495.
- Centers for Disease Control and Prevention (21 March 2020). "Information for Health Departments on Reporting Cases of COVID-19". Coronavirus Disease 2019 (COVID-19). Centers for Disease Control and Prevention. https://www.cdc.gov/coronavirus/2019-ncov/php/reporting-pui.html. Retrieved 21 March 2020.
- Government of Canada (10 February 2020). "Interim national surveillance guidelines for human infection with Coronavirus disease (COVID-19)". Government of Canada. https://www.canada.ca/en/public-health/services/diseases/2019-novel-coronavirus-infection/health-professionals/interim-guidance-surveillance-human-infection.html. Retrieved 11 April 2020.
- European Centre for Disease Prevention and Control (02 March 2020). "Case definition and European surveillance for COVID-19, as of 2 March 2020". COVID-19 Portal. European Centre for Disease Prevention and Control. https://www.ecdc.europa.eu/en/case-definition-and-european-surveillance-human-infection-novel-coronavirus-2019-ncov. Retrieved 11 April 2020.
- Green, C.; Bradley, V. (01 April 2020). "Coding guidance for new ICD-10-CM and lab testing codes for COVID-19". MGMA Stat. https://www.mgma.com/data/data-stories/coding-guidance-for-new-icd-10-cm-and-lab-testing. Retrieved 11 April 2020.
- AAP Division of Health Care Finance (12 March 2020). "How to use ICD-10-CM, new lab testing codes for COVID-19". American Academy of Pediatrics. https://www.aappublications.org/news/2020/03/12/coding031220. Retrieved 11 April 2020.
- "CDC publishes ICD-10-CM Official Guidelines for COVID-19". Revenue Cycle Advisor. 06 April 2020. https://revenuecycleadvisor.com/news-analysis/cdc-publishes-icd-10-cm-official-guidelines-covid-19. Retrieved 25 April 2020.
- Remer, E.E. (15 April 2020). "We now Have a Code for COVID-19; Here’s How to use it Correctly". ICD-10 Monitor. https://www.icd10monitor.com/we-now-have-a-code-for-covid-19-here-s-how-to-use-it-correctly. Retrieved 25 April 2020.
- National Center for Health Statistics (31 March 2020). "International Classification of Diseases, Tenth Revision, Clinical Modification (ICD-10-CM)". Centers for Disease Control and Prevention. https://www.cdc.gov/nchs/icd/icd10cm.htm. Retrieved 25 April 2020.
- Centers for Disease Control and Prevention (26 June 2020). "Commercial Laboratory Seroprevalence Survey Data". Centers for Disease Control and Prevention. https://www.cdc.gov/coronavirus/2019-ncov/cases-updates/commercial-lab-surveys.html. Retrieved 08 July 2020.
- Clabaugh, G. (19 March 2020). "Rescind the March 5, 2020 Temporary Novel Coronavirus Disease 2019 (COVID-19) Mandatory Reporting Requirement and Replace With the Following Order" (PDF). Iowa Department of Public Health. https://idph.iowa.gov/Portals/1/userfiles/7/Mandatory%20Reporting%20Order.pdf. Retrieved 25 April 2020.
- El-Kareh, R.; Hasan, O.; Schiff, G.D. (2013). "Use of health information technology to reduce diagnostic errors". BMJ Quality & Safety 22 (Suppl. 2): ii40–ii51. doi:10.1136/bmjqs-2013-001884. PMC PMC3786650. PMID 23852973. http://www.pubmedcentral.nih.gov/articlerender.fcgi?tool=pmcentrez&artid=PMC3786650.
- National Academies of Sciences, Engineering, and Medicine (2015). "Chapter 5: Technology and Tools in the Diagnostic Process". Improving Diagnosis in Health Care. The National Academies Press. pp. 217–62. doi:10.17226/21794. ISBN 9780309377720. https://www.nap.edu/read/21794/chapter/7.
- Winters-Miner, L.A.; Bolding, P.S.; Hilbe, J.M. et al. (2015). "Chapter 3: Biomedical Informatics". Practical Predictive Analytics and Decisioning Systems for Medicine. Academic Press. pp. 42–59. doi:10.1016/B978-0-12-411643-6.00003-X. ISBN 9780124116436.
- Mandl, K.D.; Overhage, J.M.; Wagner, M.M. et al. (2004). "Implementing syndromic surveillance: A practical guide informed by the early experience". JAMIA 11 (2): 141–50. doi:10.1197/jamia.M1356. PMC PMC353021. PMID 14633933. http://www.pubmedcentral.nih.gov/articlerender.fcgi?tool=pmcentrez&artid=PMC353021.
- Chawla, R.; Goswami, B.; Singh, B. et al. (2010). "Evaluating laboratory performance with quality indicators". Laboratory Medicine 41 (5): 297–300. doi:10.1309/LMS2CBXBA6Y0OWMG.
- Price, C.P. (2001). "Poing of care testing". BMJ 322 (7297): 1285–8. doi:10.1136/bmj.322.7297.1285. PMC PMC1120384. PMID 11375233. http://www.pubmedcentral.nih.gov/articlerender.fcgi?tool=pmcentrez&artid=PMC1120384.
- Ziaugra, K.; Hawrylak, V.; Bickley, T. et al. (20 March 2019). "Using analytics to manage QA and reduce laboratory errors". Medical Laboratory Observer. https://www.mlo-online.com/information-technology/lis/article/13017560/using-analytics-to-manage-qa-and-reduce-laboratory-errors. Retrieved 25 April 2020.
- Gundler, D. (23 January 2019). "POCT made easier with informatics". Medical Laboratory Observer. https://www.mlo-online.com/home/article/13017228/poct-made-easier-with-informatics. Retrieved 25 April 2020.
- Healthcare Information and Management Systems Society (2020). "Interoperability in the Healthcare Ecosystem". Healthcare Information and Management Systems. https://www.himss.org/what-interoperability. Retrieved 28 April 2020.
- Kun, L.; Coatrieux, G.; Quantin, C. et al. (2008). "Improving outcomes with interoperable EHRs and secure global health information infrastructure". Studies in Health Technology and Informatics 137: 68–79. PMID 18560070.
- Global Center for Health Innovation (n.d.). "Improving Patient Care through Interoperability". Global Center for Health Innovation.
- Maxmen, A. (2020). "Thousands of coronavirus tests are going unused in US labs". Nature 580 (7803): 312–13. doi:10.1038/d41586-020-01068-3. PMID 32273619.
- Ausiello, D.; Shaw, S. (2014). "Quantitative Human Phenotyping: The Next Frontier in Medicine". Transactions of the American Clinical and Climatological Association 125: 219–26. PMC PMC4112685. PMID 25125736. http://www.pubmedcentral.nih.gov/articlerender.fcgi?tool=pmcentrez&artid=PMC4112685.
- Edwards, R.R.; Dworkin, R.H.; Turk, D.C. et al. (2016). "Patient phenotyping in clinical trials of chronic pain treatments: IMMPACT recommendations". Pain 157 (9): 1851–71. doi:10.1097/j.pain.0000000000000602. PMC PMC5965275. PMID 27152687. http://www.pubmedcentral.nih.gov/articlerender.fcgi?tool=pmcentrez&artid=PMC5965275.
- Mousavizadeh, L.; Ghasemi, S. (2020). "Genotype and phenotype of COVID-19: Their roles in pathogenesis". Journal of Microbiology, Immunology, and Infection: 30082-7. doi:10.1016/j.jmii.2020.03.022. PMC PMC7138183. PMID 32265180. http://www.pubmedcentral.nih.gov/articlerender.fcgi?tool=pmcentrez&artid=PMC7138183.
- Gattinoni, L.; Chiumello, D.; Caironi, P. (2020). "COVID-19 pneumonia: Different respiratory treatments for different phenotypes?". Intensive Care Medicine. doi:10.1007/s00134-020-06033-2. PMC PMC7154064. PMID 32291463. http://www.pubmedcentral.nih.gov/articlerender.fcgi?tool=pmcentrez&artid=PMC7154064.
- Zhang, X.A.; Yates, A.; Vasilevsky, N. et al. (2019). "Semantic integration of clinical laboratory tests from electronic health records for deep phenotyping and biomarker discovery". npj Digital Medicine 2: 32. doi:10.1038/s41746-019-0110-4. PMC PMC6527418. PMID 31119199. http://www.pubmedcentral.nih.gov/articlerender.fcgi?tool=pmcentrez&artid=PMC6527418.
- American Association for Clinical Chemistry (21 February 2019). "Strengthening the Chain of Interoperability". CLN Stat. https://www.aacc.org/publications/cln/cln-stat/2019/february/21/strengthening-the-chain-of-interoperability. Retrieved 25 April 2020.
- Centers for Disease Control and Prevention (29 April 2020). "Contact Tracing : Part of a Multipronged Approach to Fight the COVID-19 Pandemic". Centers for Disease Control and Prevention. https://www.cdc.gov/coronavirus/2019-ncov/php/principles-contact-tracing.html. Retrieved 19 May 2020.
- Rosen, G. (2015). A History of Public Health (Revised Expanded ed.). Johns Hopkins University Press. pp. 46–47. ISBN 9781421416021. https://books.google.com/books?id=q5yeBgAAQBAJ&pg=PA46.
- Mooney, G. (2015). "Chapter Four: Combustible Material - Classrooms, Contact Tracing, and Following-Up". Intrusive Interventions: Public Health, Domestic Space, and Infectious Disease Surveillance in England, 1840–1914. University of Rochester Press. pp. 93–120. ISBN 9781580465274. https://books.google.com/books?id=P1W3CgAAQBAJ&pg=PA93.
- Davidson, R. (1996). "‘Searching for Mary, Glasgow’: Contact Tracing for Sexually Transmitted Diseases in Twentieth-Century Scotland". Social History of Medicine 9 (2): 195–214. doi:10.1093/shm/9.2.195.
- Wigfield, A.S. (1972). "27 Years of Uninterrupted Contact Tracing: The 'Tyneside Scheme'". British Journal of Venereal Diseases 48 (1): 37–50. doi:10.1136/sti.48.1.37. PMC PMC1048270. PMID 5067063. http://www.pubmedcentral.nih.gov/articlerender.fcgi?tool=pmcentrez&artid=PMC1048270.
- Lim, W.T.L. (2006). "Development of Medical Informatics in Singapore - Keeping Pace with Healthcare Challenges" (PDF). Proceedings from the 2006 Meeting of the Asia Pacific Association for Medical Informatics: 1–4. https://www.apami.org/apami2006/papers/wlim(sg).pdf.
- Zhang, Y.; Dang, Y.; Chen, Y.-D. et al. (2008). "BioPortal Infectious Disease Informatics research: Disease surveillance and situational awareness". Proceedings of the 2008 International Conference on Digital Government Research: 393–94. doi:10.5555/1367832.1367909.
- Schafer, I.J.; Knudsen, E.; McNamara, L.A. et al. (2016). "The Epi Info Viral Hemorrhagic Fever (VHF) Application: A Resource for Outbreak Data Management and Contact Tracing in the 2014–2016 West Africa Ebola Epidemic". The Journal of Infectious Diseases 214 (Suppl. 3): S122–S136. doi:10.1093/infdis/jiw272. PMID 27587635.
- Ha, Y.P.; Tesfalul, M.A.; Littman-Quinn, R. et al. (2016). "Evaluation of a Mobile Health Approach to Tuberculosis Contact Tracing in Botswana". Journal of Health Communication 21 (10): 1115-21. doi:10.1080/10810730.2016.1222035. PMC PMC6238947. PMID 27668973. http://www.pubmedcentral.nih.gov/articlerender.fcgi?tool=pmcentrez&artid=PMC6238947.
- American Hospital Association (04 May 2020). "CDC sets preliminary standards for digital COVID-19 contact tracing tools". American Hospital Association. https://www.aha.org/news/headline/2020-05-04-cdc-sets-preliminary-standards-digital-covid-19-contact-tracing-tools. Retrieved 19 May 2020.
- Yan, H. (15 May 2020). "Contact tracing 101: How it works, who could get hired, and why it's so critical in fighting coronavirus now". CNN Health. https://www.cnn.com/2020/04/27/health/contact-tracing-explainer-coronavirus/index.html. Retrieved 19 May 2020.
- Fortin, J. (18 May 2020). "So You Want to Be a Contact Tracer?". The New York Times. https://www.nytimes.com/2020/05/18/health/coronavirus-contact-tracing-jobs.html. Retrieved 19 May 2020.
- Waltz, E. (25 March 2020). "Halting COVID-19: The Benefits and Risks of Digital Contact Tracing". IEEE Spectrum. https://spectrum.ieee.org/the-human-os/biomedical/ethics/halting-covid19-benefits-risks-digital-contact-tracing. Retrieved 19 May 2020.
- Lew, C.; Anderson, F. (14 April 2020). "‘Digital Contact Tracing’ — Advantages, Risks, & Post-COVID Applications". DeciBio. https://www.decibio.com/2020/04/14/digital-contact-tracing-2020-4-14/. Retrieved 19 May 2020.
- Huang, Y.; Sun, M.; Sui, Y. (15 April 2020). "How Digital Contact Tracing Slowed Covid-19 in East Asia". Harvard Business Review. https://hbr.org/2020/04/how-digital-contact-tracing-slowed-covid-19-in-east-asia. Retrieved 19 May 2020.
- Knowledge Centre Data & Society (19 May 2020). "Survey: 51% has no objection to a corona-app, under strict conditions". Knowledge Centre Data & Society. https://data-en-maatschappij.ai/en/news/survey-corona-app. Retrieved 19 May 2020.
- "The EDPB Guidelines on digital tools against Covid-19, a brief comment". KU Leuven Centre for IT & IP Law. 12 May 2020. https://www.law.kuleuven.be/citip/blog/the-edpb-guidelines-on-digital-tools-against-covid-19-a-brief-comment/. Retrieved 19 May 2020.
- European Data Protection Board (April 2020). "Guidelines 04/2020 on the use of location data and contact tracing tools in the context of the COVID-19 outbreak". European Data Protection Board. https://edpb.europa.eu/our-work-tools/our-documents/usmernenia/guidelines-042020-use-location-data-and-contact-tracing_en. Retrieved 19 May 2020.
- Nazeer, T. (07 April 2020). "Digital Surveillance and 'Technological Totalitarianism'". Byline Times. https://bylinetimes.com/2020/04/07/the-coronavirus-crisis-digital-surveillance-and-technological-totalitarianism/. Retrieved 15 May 2020.
- Bay, J.; Kek, J.; Tan, A. et al. (2020). BlueTrace: A privacy-preserving protocol for community-driven contact tracing across borders. pp. 1–9. https://bluetrace.io/static/bluetrace_whitepaper-938063656596c104632def383eb33b3c.pdf.
Citation information for this chapter
Chapter: 3. Workflow and information management for COVID-19 (and other respiratory diseases)
Edition: Edition 1.1
Title: COVID-19 Testing, Reporting, and Information Management in the Laboratory
Author for citation: Shawn E. Douglas
License for content: Creative Commons Attribution-ShareAlike 4.0 International
Publication date: July 2020