View source for Journal:Diagnostic informatics: The role of digital health in diagnostic stewardship and the achievement of excellence, safety, and value

{{Infobox journal article
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|title_full   = Diagnostic informatics: The role of digital health in diagnostic stewardship and the achievement of excellence, safety, and value
|journal      = ''Frontiers in Digital Health''
|authors      = Georgiou, Andrew; Li, Julie; Hardie, Rae-Anne; Wabe, Nasir; Horvath, Andrea R.; Post, Jeffrey J.; Eigenstetter, Alex; Lindeman, Robert; Lam, Que; Badrick, Tony; Pearce, Christopher
|affiliations = Macquarie University, Prince of Wales Hospital, University of New South Wales, New South Wales Health Pathology, Austin Health, Royal College of Pathologists of Australasia, Outcome Health, Monash University
|contact      = Email: andrew dot georgiou at mq dot edu dot au
|editors      = Chapman, Wendy
|pub_year     = 2021
|vol_iss      = '''3'''
|at           = 659652
|doi          = [http://dx.doi.org/10.3389/fdgth.2021.659652 10.3389/fdgth.2021.659652]
|issn         = 2673-253X
|license      = [http://creativecommons.org/licenses/by/4.0/ Creative Commons Attribution 4.0 International]
|website      = [https://www.frontiersin.org/articles/10.3389/fdgth.2021.659652/full https://www.frontiersin.org/articles/10.3389/fdgth.2021.659652/full]
|download     = [https://www.frontiersin.org/articles/10.3389/fdgth.2021.659652/pdf https://www.frontiersin.org/articles/10.3389/fdgth.2021.659652/pdf] (PDF)
}}
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==Abstract==
Diagnostic investigations (i.e., pathology [[laboratory]] analysis and [[medical imaging]]) aim to increase the certainty of the presence of or absence of disease by supporting the process of differential diagnosis, support clinical management, and monitor a patient's trajectory (e.g., disease progression or response to treatment). Digital health—defined as the collection, storage, retrieval, transmission, and utilization of data, [[information]], and knowledge to support healthcare—has become an essential component of the diagnostic investigational process, helping to facilitate the accuracy and timeliness of information transfer and enhance the effectiveness of decision-making processes. Digital health is also important to diagnostic stewardship, which involves coordinated guidance and interventions to ensure the appropriate utilization of [[Diagnostic laboratory|diagnostic tests]] for therapeutic decision-making. Diagnostic stewardship and [[Informatics (academic field)|informatics]] are thus important in efforts to establish shared decision-making. This is because they contribute to the establishment of shared information platforms (which enable patients to read, comment on, and share in decisions about their care) based on timely and meaningful communication. 

This paper will outline key diagnostic informatics and stewardship initiatives across three interrelated fields: (1) diagnostic error and the establishment of outcome-based diagnostic research, (2) the safety and effectiveness of test result management and follow-up, and (3) digitally enhanced [[Clinical decision support system|clinical decision support systems]].

'''Keywords''': pathology, diagnostic error, outcomes based assessment, evaluation, safety, health informatics, decision support, test result follow-up

==Introduction==
Diagnostic investigations and tests (e.g., pathology and [[medical imaging]]) involve the observation of personal characteristics, symptoms, signs, and history. Their aim is to increase certainty of the presence of or absence of disease (or in the case of the differential diagnosis process, distinguish between different possible diagnoses), support clinical management, and monitor a patient's trajectory (e.g., during or after treatment).<ref name="KnottnerusEval02">{{cite journal |title=Evaluation of diagnostic procedures |journal=BMJ |author=Knottnerus, J.A.; van Weel, C.; Muris, J.W.M. |volume=324 |issue=7335 |pages=477-80 |year=2002 |doi=10.1136/bmj.324.7335.477 |pmid=11859054 |pmc=PMC1122397}}</ref> Most recently, [[Diagnostic laboratory|diagnostic investigations]] have had a pivotal role in the response to the [[SARS-CoV-2]] [[pandemic]] through [[laboratory]] testing and [[Epidemiology|epidemiological]] surveillance of viral infection, not only from its detection and diagnosis (e.g., via [[Molecular diagnostics|molecular testing]] by [[Polymerase chain reaction|real-time polymerase chain reaction]] [RT-PCR]) but also its prognostication, treatment, monitoring (e.g., testing related to comorbidities), and recovery (aided by anti-SARS-CoV-2 monoclonal antibodies).

Digital health—defined as the collection, storage, retrieval, transmission, and utilization of data, [[information]], and knowledge to support healthcare<ref name="RowlandsWhat19">{{cite web |url=https://www.hisa.org.au/wp-content/uploads/2019/12/What_is_Digital_Health.pdf?x97063 |format=PDF |title=What is digital health? And why does it matter? |author=Rowlands, D. |publisher=Digital Health Workforce Academy |date=02 December 2019}}</ref>—contributes to effective decision-making during diagnostic investigations by improving the ability to gather, organize, and display information, or by enhancing timely access to diagnostic reference information, as a result facilitating follow-up and providing valuable feedback to clinicians and patients.<ref name="El-KarehUse13">{{cite journal |title=Use of health information technology to reduce diagnostic errors |journal=BMJ Quality and Safety |author=El-Kareh, R.; Hasan, O.; Schiff, G.D. |volume=22 |issue=Suppl 2 |pages=ii40-ii51 |year=2013 |doi=10.1136/bmjqs-2013-001884 |pmid=23852973 |pmc=PMC3786650}}</ref> [[Health informatics|Diagnostic informatics]]—the use of digital health tools to facilitate the accuracy and timeliness of health information transfer and enhance the effectiveness of the decision-making processes<ref name="AllerElect12">{{cite book |chapter=Electronic Health Records |title=Pathology Informatics: Theory and Practice |author=Aller, R.D.; Georgiou, A.; Pantanowitz, L. |editor=Pantanowitz, L.; Tuthill, J.M.; Balis, U. |pages=217–30 |year=2012 |isbn=9780891896197}}</ref>—is fundamental for the safe and effective management of diagnostic investigations and the data that comes from them. Diagnostic informatics is thusly a key component of successful diagnostic stewardship, which encompasses the coordination of guidance and interventions to ensure the appropriate utilization of diagnostic tests for therapeutic decision-making.<ref name="WHODiag16">{{cite web |url=https://www.who.int/publications/i/item/WHO-DGO-AMR-2016.3 |title=Diagnostic stewardship: A guide to implementation in antimicrobial resistance surveillance sites |author=Global Antimicrobial Resistance Surveillance System |publisher=World Health Organization |year=2016}}</ref>

This paper will outline the importance of key diagnostic informatics and digital health stewardship initiatives across three interrelated areas: (1) diagnostic error and the establishment of outcome-based diagnostic research; (2) the safety and effectiveness of test result management and follow-up, including the critical role that patients can play in the process; and (3) digitally enhanced [[Clinical decision support system|clinical decision support systems]].

==Diagnostic error and the establishment of outcome-based diagnostic informatics research==
Diagnostic error is a major problem in healthcare, contributing to ~10% of patient deaths and between 6 to 17% of [[hospital]] adverse events.<ref name="BaloghImprov15">{{cite book |title=Improving Diagnosis in Health Care |editor=Balogh, E.P.; Miller, B.T.; Ball, J.R. |publisher=The National Academies Press |year=2015 |isbn=9780309377706 |doi=10.17226/21794}}</ref> Diagnostic error involves either the failure to establish an accurate and timely explanation of the patient's health problem(s) or the failure to effectively communicate that explanation to the patient.<ref name="BaloghImprov15" /> In Australia, medico-legal data show that diagnostic error is implicated in half of medical negligence claims involving general practitioners.<ref name="BirdReduc18">{{cite web |url=https://www1.racgp.org.au/newsgp/professional/reducing-diagnostic-error |title=Reducing diagnostic error |work=newsGP |author=Bird, S. |date=23 February 2018 |accessdate=18 January 2019}}</ref>

Many factors can contribute to diagnostic error, including the lack of an integrated care pathway; the involvement of multiple specialists; problems with collaboration and communication among clinicians, patients, and their families; lack of infrastructure to support the diagnostic process; and inadequate attention to understanding the health problem and its causes.<ref name="BaloghImprov15" /> Existing evidence shows that in acute care, an estimated 45% of laboratory testing is underutilized (i.e., when one or more tests should have been undertaken but weren't) and 21% is overutilized (i.e., when one or more tests were unnecessary or repeated within an inappropriate time frame).<ref name="ZhiTheLand13">{{cite journal |title=The landscape of inappropriate laboratory testing: A 15-year meta-analysis |journal=PLoS One |author=Zhi, M.; Ding, E.L.; Theisen-Toupal, J. et al. |volume=8 |issue=11 |at=e78962 |year=2013 |doi=10.1371/journal.pone.0078962 |pmid=24260139 |pmc=PMC3829815}}</ref>

There is currently a lack of a systematic outcomes-based approach to identify and monitor the prevalence and impact of diagnostic errors. In part, this is because the typical relationship between a test and a health outcome is indirect. It is made harder still by the existence of data silos across different clinical and care settings (e.g., pathology, medical imaging, medical records, emergency, and hospital administration systems), which limit the ability to link test results and referrals to the different components of the patient journey (e.g., treatment and outcome). Despite the existence of evidence-based guidelines to encourage best practice utilization of diagnostic tests, there are very few studies on how these guidelines have impacted testing patterns and patient outcomes. This was noted by a recent international scoping review that drew attention to the significant lack of strategies for optimizing test utilization and improving patient outcomes in general practice.<ref name="ElwenspoekWhat20">{{cite journal |title=What methods are being used to create an evidence base on the use of laboratory tests to monitor long-term conditions in primary care? A scoping review |journal=Family Practice |author=Elwenspoek, M.M.C.; Scott, L.J.; Alsop, K. et al. |volume=37 |issue=6 |pages=845-853 |year=2020 |doi=10.1093/fampra/cmaa074 |pmid=32820328 |pmc=PMC7759753}}</ref>

Linked and integrated digital health data sources and repositories across hospital, pathology, and primary care provide a means to identify, measure, and monitor the quality of care. They can deliver key outcome-based measures of diagnostic utilization such as patient outcome or hospital admissions, which can be used to evaluate the impact of vital interventions (e.g., electronic decision support). This is the premise underlying critical initiatives like the New South Wales (NSW) Health Pathology Atlas of Variation (in collaboration with the NSW Emergency Care Institute and NSW eHealth Integrated Care), designed to create a statewide quality improvement project.<ref name="ScowenEval20">{{cite journal |title=Evaluating the long-term effects of a data-driven approach to reduce variation in emergency department pathology investigations: study protocol for evaluation of the NSW Health Pathology Atlas of variation |journal=BMJ Open |author=Scowen, C.; Wabe, N.; Eigenstetter, A. et al. |volume=10 |issue=10 |at=e039437 |year=2020 |doi=10.1136/bmjopen-2020-039437 |pmid=33046472 |pmc=PMC7552857}}</ref>

As an example of the potential of outcome-focused studies to impact on patient care, we investigated the incidence of acute kidney injury (AKI) over a five-year period (2009–2013) across four NSW hospitals.<ref name="CampbellUnder20">{{cite journal |title=Under-detection of acute kidney injury in hospitalised patients: A retrospective, multi-site, longitudinal study |journal=Internal Medicine Journal |author=Campbell, C.A.; Ling, L.; Kotwal, S. et al. |volume=50 |issue=3 |pages=307-314 |year=2020 |doi=10.1111/imj.14264 |pmid=30816607}}</ref> Patients with AKI were identified using the serum creatinine-based definition as described in the international consensus guidelines published by the “Kidney Disease: Improving Global Outcomes” (KDIGO) Work Group. This required programming of a complex algorithm, which was run on more than two million creatinine results to detect the presence of AKI. The study found that only 15.9% of hospitalizations with AKI stage 1 were coded as such<ref name="CampbellUnder20" />, thus highlighting the need for an evidence-based laboratory AKI decision support aid. Other examples of the value of linked data helping to power programs of research include measuring the effects of [[electronic medical record]]s on healthcare<ref name="GeorgiouMonit13">{{cite journal |title=Monitoring the impact of the electronic medical record on the quality of laboratory test ordering practices |journal=Studies in Health Technologies and Informatics |author=Georgiou, A.; Vecellio, E.; Toouli, G. et al. |volume=188 |pages=33–8 |year=2013 |pmid=23823285}}</ref><ref name="VecellioExam15">{{cite book url=https://www.health.gov.au/internet/main/publishing.nsf/Content/0E3978777CE9637BCA2580C200088F78/$File/Final Report - Variation in Hospital Pathology - Macquarie University.pdf |format=PDF |title=Examination of variation in hospital pathology investigations by Diagnosis-Related Groups and associations with outcomes and costs |author=Vecellio, E.; Li, L.; Xiong, J. et al. |publisher=Centre for Health Systems and Safety Research |year=2015 |isbn=9781741384307}}</ref>, and the impact of the rapid flu tests on emergency department (ED) performance and patient outcomes.<ref name="WabeTheImp19">{{cite journal |title=The impact of rapid molecular diagnostic testing for respiratory viruses on outcomes for emergency department patients |journal=Medical Journal of Australia |author=Wabe, N.; Li, L.; Lindeman, R. et al. |volume=210 |issue=7 |pages=316–20 |year=2019 |doi=10.5694/mja2.50049 |pmid=30838671 |pmc=PMC6617970}}</ref>


==References==
{{Reflist|colwidth=30em}}

==Notes==
This presentation is faithful to the original, with only a few minor changes to presentation, though grammar and word usage was substantially updated for improved readability. In some cases important information was missing from the references, and that information was added.

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