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===3.3 Additional benefits and | ===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 record]]s, from bioinformatics tools for peptide and protein modelling 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. | 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 record]]s, from bioinformatics tools for peptide and protein modelling 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. | ||
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In fact, interoperability issues have come up during the global laboratory response to the COVID-19 pandemic. | In fact, interoperability issues have come up during the global laboratory response to the COVID-19 pandemic. | ||
The value of patient phenotyping data is also useful in the fight against known and novel viruses, as well as a broad variety of non-viral diseases. Phenotypes represent a genetic analysis of the collection of observable traits of an organism, traits caused by the interaction of its genome with the environment. 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."<ref name="AusielloQuant14">{{cite journal |title=Quantitative Human Phenotyping: The Next Frontier in Medicine |journal=Transactions of the American Clinical and Climatological Association |author=Ausiello, D.; Shaw, S. |volume=125 |pages=219–26 |year=2014 |pmid=25125736 |pmc=PMC4112685}}</ref> Whether it's identifying "the measurable phenotypic characteristics of patients that are most predictive of individual variation" in treatment outcomes for chronic pain<ref name="EdwardsPatient16">{{cite journal |title=Patient phenotyping in clinical trials of chronic pain treatments: IMMPACT recommendations |journal=Pain |author=Edwards, R.R.; Dworkin, R.H.; Turk, D.C. et al. |volume=157 |issue=9 |pages=1851–71 |year=2016 |doi=10.1097/j.pain.0000000000000602 |pmid=27152687 |pmc=PMC5965275}}</ref> or COVID-19<ref name="MousavizadehGenotype20">{{cite journal |title=Genotype and phenotype of COVID-19: Their roles in pathogenesis |journal=Journal of Microbiology, Immunology, and Infection |author=Mousavizadeh, L.; Ghasemi, S. |pages=30082-7 |year=2020 |doi=10.1016/j.jmii.2020.03.022 |pmid=32265180 |pmc=PMC7138183}}</ref><ref name="GattinoniCOVID20">{{cite journal |title=COVID-19 pneumonia: Different respiratory treatments for different phenotypes? |journal=Intensive Care Medicine |author=Gattinoni, L.; Chiumello, D.; Caironi, P. |year=2020 |doi=10.1007/s00134-020-06033-2 |pmid=32291463 |pmc=PMC7154064}}</ref> | The value of patient phenotyping data is also useful in the fight against known and novel viruses, as well as a broad variety of non-viral diseases. Phenotypes represent a genetic analysis of the collection of observable traits of an organism, traits caused by the interaction of its genome with the environment. 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."<ref name="AusielloQuant14">{{cite journal |title=Quantitative Human Phenotyping: The Next Frontier in Medicine |journal=Transactions of the American Clinical and Climatological Association |author=Ausiello, D.; Shaw, S. |volume=125 |pages=219–26 |year=2014 |pmid=25125736 |pmc=PMC4112685}}</ref> Whether it's identifying "the measurable phenotypic characteristics of patients that are most predictive of individual variation" in treatment outcomes for chronic pain<ref name="EdwardsPatient16">{{cite journal |title=Patient phenotyping in clinical trials of chronic pain treatments: IMMPACT recommendations |journal=Pain |author=Edwards, R.R.; Dworkin, R.H.; Turk, D.C. et al. |volume=157 |issue=9 |pages=1851–71 |year=2016 |doi=10.1097/j.pain.0000000000000602 |pmid=27152687 |pmc=PMC5965275}}</ref> or COVID-19<ref name="MousavizadehGenotype20">{{cite journal |title=Genotype and phenotype of COVID-19: Their roles in pathogenesis |journal=Journal of Microbiology, Immunology, and Infection |author=Mousavizadeh, L.; Ghasemi, S. |pages=30082-7 |year=2020 |doi=10.1016/j.jmii.2020.03.022 |pmid=32265180 |pmc=PMC7138183}}</ref><ref name="GattinoniCOVID20">{{cite journal |title=COVID-19 pneumonia: Different respiratory treatments for different phenotypes? |journal=Intensive Care Medicine |author=Gattinoni, L.; Chiumello, D.; Caironi, P. |year=2020 |doi=10.1007/s00134-020-06033-2 |pmid=32291463 |pmc=PMC7154064}}</ref> | ||
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.<ref name="ZhangSemantic19">{{cite journal |title=Semantic integration of clinical laboratory tests from electronic health records for deep phenotyping and biomarker discovery |journal=npj Digital Medicine |author=Zhang, X.A.; Yates, A.; Vasilevsky, N. et al. |volume=2 |at=32 |year=2019 |doi=10.1038/s41746-019-0110-4 |pmid=31119199 |pmc=PMC6527418}}</ref> They address one of the more difficult aspects of their research, in 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: | |||
<blockquote>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.</blockquote> | |||
====3.3.1 Bioinformatics==== | ====3.3.1 Bioinformatics==== |
Revision as of 19:55, 25 April 2020
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 public health 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 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.[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]
3.2.1 ICD and CPT coding
Related are any internal reporting requirements, particularly for test reporting in labs and medical facilities (and in some cases, reporting to local health departments requires internal reporting adjustments). The International Statistical Classification of Diseases and Related Health Problems (ICD) is a commnoly 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 (and should not be considered legally binding advice), but see the referenced material (and following citations) for more details concerning those codes[8][9][10][11]:
- 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[12]—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[3][4], health care providers, facilities, and laboratories are being held responsible for sending case date 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). However, the 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."[13] 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.
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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 modelling 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 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."[14] 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.[15]
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."[16] Additionally, through the use of syndromic surveillance systems (tools aiding in the detection of indicators leading up to disease diagnosis for individuals and populations[17]), 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.[16]
In the clinical laboratory, informatics systems have been influencing workflow improvements and improved sevice delivery in the lab for more than five decades.[18] And while improvements have been seen in the laboratory from not only the introduction of computerized systems[14][15][19] but also the realization of quality control[20] and point-of-care testing[21], more challenges remain. For example, quality management in the laboratory is still often a manual, time-consuming activity. While the LIS and LIMS 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 quality and efficiencies in the lab.[22] 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."[23]
System interoperability also poses benefits and challenges to clinical disease testing and prevention. Interoperability is defined as ... Improving interoperability among clinical informatics systems is recognized as an important step towards improving health outcomes.[24][25] The National Academies of Sciences, Engineering, and Medicine had much to say on this topic in their 2015 publication Improving Diagnosis in Health Care[15]:
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 EHRs and the laboratory and other clinical information systems that feed medical diagnostic information into the EHRs[15]:
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.
The value of patient phenotyping data is also useful in the fight against known and novel viruses, as well as a broad variety of non-viral diseases. Phenotypes represent a genetic analysis of the collection of observable traits of an organism, traits caused by the interaction of its genome with the environment. 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."[26] Whether it's identifying "the measurable phenotypic characteristics of patients that are most predictive of individual variation" in treatment outcomes for chronic pain[27] or COVID-19[28][29]
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.[30] They address one of the more difficult aspects of their research, in 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.
3.3.1 Bioinformatics
References
- ↑ 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.
- ↑ 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.
- ↑ 3.0 3.1 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.
- ↑ 4.0 4.1 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.
- ↑ 5.0 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.
- ↑ 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 (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.
- ↑ 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.
- ↑ 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. 6 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.
- ↑ 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.
- ↑ 14.0 14.1 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.
- ↑ 15.0 15.1 15.2 15.3 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.
- ↑ 16.0 16.1 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.
- ↑ 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.
- ↑ 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. https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4204239.
- ↑ 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.
- ↑ 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. https://www.ncbi.nlm.nih.gov/pmc/articles/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.
- ↑ 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 (23 November 2024). "Improving Patient Care through Interoperability". Global Center for Health Innovation.
- ↑ 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. https://www.ncbi.nlm.nih.gov/pmc/articles/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. https://www.ncbi.nlm.nih.gov/pmc/articles/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. https://www.ncbi.nlm.nih.gov/pmc/articles/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. https://www.ncbi.nlm.nih.gov/pmc/articles/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. https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6527418.