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| [[File:Electronic medical record.jpg|right|460px]]System interoperability also poses benefits and challenges to clinical disease testing and prevention. Interoperability is defined as “the ability of different information systems, devices and applications (‘systems’) to access, exchange, integrate and cooperatively use data in a coordinated manner” to ensure timely, portable information and improved health outcomes.<ref name=”HIMSSInterop20”>{{cite web |url=https://www.himss.org/resources/interoperability-healthcare |title=Interoperability in Healthcare |author=Healthcare Information and Management Systems Society |publisher=Healthcare Information and Management Systems |date=2020 |accessdate=28 April 2020}}</ref> Improving interoperability among clinical informatics systems is recognized as an important step towards improving health outcomes.<ref name="KunImprov08">{{cite journal |title=Improving outcomes with interoperable EHRs and secure global health information infrastructure |journal=Studies in Health Technology and Informatics |author=Kun, L.; Coatrieux, G.; Quantin, C. et al. |volume=137 |pages=68–79 |year=2008 |pmid=18560070}}</ref><ref name="GCHIImproving">{{cite web |url=http://s3.amazonaws.com/rdcms-himss/files/production/public/Improving-Patient-Carethrough-Interoperability.pdf |format=PDF |title=Improving Patient Care through Interoperability |author=Global Center for Health Innovation |publisher=Global Center for Health Innovation |date=n.d. |accessdate=17 September 2021}}</ref> The National Academies of Sciences, Engineering, and Medicine had much to say on this topic in their 2015 publication ''Improving Diagnosis in Health Care''<ref name="NASEMImprov15">{{cite book |url=https://www.nap.edu/read/21794/chapter/7 |chapter=Chapter 5: Technology and Tools in the Diagnostic Process |title=Improving Diagnosis in Health Care |author=National Academies of Sciences, Engineering, and Medicine |publisher=The National Academies Press |pages=217–62 |year=2015 |doi=10.17226/21794 |isbn=9780309377720}}</ref> :
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| <blockquote>Improved interoperability across different health care organizations—as well as across laboratory and [[radiology information system]]s—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.</blockquote>
| | ==''Introduction to Quality and Quality Management Systems''== |
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| | The goal of this short volume is to act as an introduction to the quality management system. It collects several articles related to quality, quality management, and associated systems. |
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| In particular, they discuss an additional concern, one that still causes issues today: interfaces between [[electronic health record]]s (EHR) and the laboratory and other clinical information systems that feed medical diagnostic information into the EHRs<ref name="NASEMImprov15" />:
| | ;1. What is quality? |
| | :''Key terms'' |
| | :[[Quality (business)|Quality]] |
| | :[[Quality assurance]] |
| | :[[Quality control]] |
| | :''The rest'' |
| | :[[Data quality]] |
| | :[[Information quality]] |
| | :[[Nonconformity (quality)|Nonconformity]] |
| | :[[Service quality]] |
| | ;2. Processes and improvement |
| | :[[Business process]] |
| | :[[Process capability]] |
| | :[[Risk management]] |
| | :[[Workflow]] |
| | ;3. Mechanisms for quality |
| | :[[Acceptance testing]] |
| | :[[Conformance testing]] |
| | :[[Clinical quality management system]] |
| | :[[Continual improvement process]] |
| | :[[Corrective and preventive action]] |
| | :[[Good manufacturing practice]] |
| | :[[Malcolm Baldrige National Quality Improvement Act of 1987]] |
| | :[[Quality management]] |
| | :[[Quality management system]] |
| | :[[Total quality management]] |
| | ;4. Quality standards |
| | :[[ISO 9000]] |
| | :[[ISO 13485]] |
| | :[[ISO 14000|ISO 14001]] |
| | :[[ISO 15189]] |
| | :[[ISO/IEC 17025]] |
| | :[[ISO/TS 16949]] |
| | ;5. Quality in software |
| | :[[Software quality]] |
| | :[[Software quality assurance]] |
| | :[[Software quality management]] |
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| <blockquote>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.</blockquote> | | <!--Place all category tags here--> |
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| 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.<ref name="MaxmenThousands20">{{cite journal |title=Thousands of coronavirus tests are going unused in US labs |journal=Nature |author=Maxmen, A. |volume=580 |issue=7803 |pages=312–13 |year=2020 |doi=10.1038/d41586-020-01068-3 |pmid=32273619}}</ref> As it turns out, [[HL7]]- and other standard-based interfaces have long been expensive for many vendors to implement<ref name="John3504HL7_11">{{cite web |url=https://community.spiceworks.com/topic/175107-hl7-interface-cost-and-maintenance |title=HL7 Interface cost and maintenance |author=John3504 |work=Spiceworks |date=07 December 2011 |accessdate=25 April 2020}}</ref>, 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 system]]s (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.
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| 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."<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>, phenotypes have utility in the clinical sector.
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| 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> 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<ref name="ZhangSemantic19" />:
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| <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>
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| 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 interface]]s (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.<ref name="AACCStrength19">{{cite web |url=https://www.aacc.org/cln/cln-stat/2019/february/21/strengthening-the-chain-of-interoperability |title=Strengthening the Chain of Interoperability |author=American Association for Clinical Chemistry |work=CLN Stat |date=21 February 2019 |accessdate=17 September 2021}}</ref> On a more positive note, the Office of the National Coordinator for Health Information Technology (ONC) updated its Interoperability Standards Advisory (ISA) [https://www.healthit.gov/isa/covid-19 Vocabulary/Code Set/Terminology page] in November to better "highlight critical public health interoperability needs on COVID-19 in an easily accessible way." Replying to the ONC, HIMSS's Senior Director Jeff Coughlin goes on to add<ref name="CoughlinCOVID20">{{cite web |url=https://www.healthit.gov/isa/covid-19 |archiveurl=https://web.archive.org/web/20201110225342/https://www.healthit.gov/isa/covid-19 |title=COVID-19 Novel Coronavirus Pandemic |author=Coughlin, J. |work=ISA - Vocabulary/Code Set/Terminology |publisher=Office of the National Coordinator |archivedate=10 November 2020 |accessdate=17 September 2021}}</ref>:
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| <blockquote>There is a growing need to consider data exchange for home settings and considerations around device interoperability. There are a number of applications in use and this setting requires work across a number of systems (emergency medical services, hospital electronic health records, telemedicine system [synchronous and asynchronous] and, remote patient monitoring and device management). ISA should provide guidance on specific standards to assist in exchange with this setting."</blockquote>
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| 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.
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| ==References==
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