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3. Workflow and information management for COVID-19 (and other pandemics)

3.1 Laboratory informatics and workflow management

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 activites 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.[1] Analytical epidemiology allows for the testing of those hypotheses using both experimental and obsevational studies, as well as control groups. Similarly, the collection and use of quality experimental and observational data is vital for proving or disproving hypotheses.[2] In both cases, proper reporting of data is critical to the success of epidemiologists' response to outbreaks and pandemics, as well as the credibility of their research.[3][4]

The proper reporting of COVID-19 case data is no exception. In the United States, the 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."[5] 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 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.[5] 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.[6] And in the European Union, member countries and the U.K. are asked to report through the Early Warning and Response System.[7]

Somewhat related are any internal reporting requirements, particularly for test reporting in labs and medical facilities. The International Statistical Classification of Diseases and Related Health Problems (ICD) is a 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. Green and Bradley provide insight into these additions[8], as does the American Academy of Pediatrics.[9]

Laboratories analyzing specimens for SARS-CoV-2 therefore must be equipped to not only handle analytical testing and test orders using the new test codes, but they also must be able to quickly and accurately transfer vital case information to the appropriate health authority.

Table 1. U.S. state-based COVID-19 reporting requirements
State Electronic file (Y/N) Fax? (Y/N) Forms for reporting Contact Additional details
Alabama Y N Novel Coronavirus Report Form ALNEDSSsupport@adph.state.al.us "Providers who are either reporting laboratory results for patients tested by a commercial or clinical laboratory (not tested by the BCL) or reporting deaths among patients with positive results must complete" the report form. "Laboratories are required to report all negative and positive COVID-19 virus test results electronically (faxes do not count). If not already enrolled, laboratories will need to manually enter test results directly into the surveillance system." Email the contact to enroll staff access to the system.
Alaska Y Y Confidential Infectious Disease Report Form COVID Reporting Hotline: 1-877-469-8067
 
Urgent situation: 907-269-8000 or 800-478-0084 (after-hours)
 
ELR: megan.tompkins@alaska.gov
"Providers must report laboratory-confirmed cases of COVID-19 to SOE by either leaving a message on the COVID Reporting Hotline (1-877-469-8067) or via fax using the standard Infectious Disease Report Form." "[A]ll results both positive and negative must be reported via either integration into existing electronic laboratory reporting (ELR) data feeds or fax (907-563-7868). Please email Megan Tompkins ... to inform us about how your facility will report."
Arizona Y Y Sample Aggregate Weekly Submission Form
Sample ADHS CSV File
Sample ADHS Excel File
Fax: (602) 364-3199
 
ELR: elr@azdhs.gov
"[A] laboratory as defined in A.R.S. § 36-451(4) shall report all COVID-19 test results (positive and negative) to the Arizona Department of Health Services in an electronic format as follows: a. For laboratories reporting to the Arizona Department of Health Services through electronic lab reporting ('ELR'), results of all COVID-19 tests; b. For laboratories not reporting to the Arizona Department of Health Services through ELR, a weekly aggregate number of total COVID-19 tests performed and their results." If reporting is accomplished through mail or fax, use the Sample Aggregate Weekly Submission Form. "Aggregate reporting does NOT replace mandatory laboratory reporting requirements per Arizona Administrative Code R9-6-204."
 
"ADHS has created a new electronic option for reporting results of COVID-19 in-house testing as an alternative to Health Level Seven (HL7) electronic laboratory reporting or direct entry into MEDSIS. This can now be accomplished through a standardized spreadsheet or comma separated value (CSV) file format."
Arkansas
California
Colorado Y Y General Communicable Disease Reporting Form Fax: (303) 782-0338
 
CEDRS: lavelle.fernandez@state.co.us
 
ELR: andrew.horvath@state.co.us
The state doesn't appear to have specific rules or a reporting form for laboratory reporting of COVID-19. Presumably labs are reporting COVID-19 as is required for any communicable disease. The state lists multiple ways to report a case: calling, faxing a General Communicable Disease Reporting Form, through CEDRS, or by using ELR. The also offers guidance for healthcare providers. "Any suspected or confirmed case or outbreak of COVID-19 should immediately be reported to the local or state public health agency. To report, utilize the [Outbreak COVID-19 Outbreak Report Form]. Send this form to your local public health agency OR to CDPHE by securely emailing a completed form to: cdphe_haioutbreak@state.co.us."


3.3 Additional benefits of laboratory informatics in disease testing and public health

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."[10] 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.[11]

From a public health perspective, the application of informatics to disease surveillance, reporting, and health 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."[12] Additionally, through the use of syndromic surveillance systems (tools aiding in the detection of indicators leading up to disease diagnosis for individuals and populations[13]), 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.[12]

https://www.nature.com/articles/s41746-019-0110-4 https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4204239/ https://www.nap.edu/read/21794/chapter/7#241 https://www.mlo-online.com/home/article/13017228/poct-made-easier-with-informatics

3.3.1 Bioinformatics

References

  1. 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. https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4213216. 
  2. 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. 
  3. 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. 
  4. 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. https://www.ncbi.nlm.nih.gov/pmc/articles/PMC2020495. 
  5. 5.0 5.1 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. 
  6. 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. 
  7. European Centre for Disease Prevention and Control (2 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. 
  8. Green, C.; Bradley, V. (1 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. 
  9. 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. 
  10. 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. https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3786650. 
  11. 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. 
  12. 12.0 12.1 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. 
  13. 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. https://www.ncbi.nlm.nih.gov/pmc/articles/PMC353021.