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

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Full article title Diagnostic informatics: The role of digital health in diagnostic stewardship and the achievement of excellence, safety, and value
Journal Frontiers in Digital Health
Author(s) 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
Author affiliation(s) 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
Primary contact Email: andrew dot georgiou at mq dot edu dot au
Editors Chapman, Wendy
Year published 2021
Volume and issue 3
Article # 659652
DOI 10.3389/fdgth.2021.659652
ISSN 2673-253X
Distribution license Creative Commons Attribution 4.0 International
Website https://www.frontiersin.org/articles/10.3389/fdgth.2021.659652/full
Download 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 tests for therapeutic decision-making. Diagnostic stewardship and 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 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).[1] Most recently, diagnostic investigations have had a pivotal role in the response to the SARS-CoV-2 pandemic through laboratory testing and epidemiological surveillance of viral infection, not only from its detection and diagnosis (e.g., via molecular testing by 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[2]—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.[3] 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[4]—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.[5]

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 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.[6] 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.[6] In Australia, medico-legal data show that diagnostic error is implicated in half of medical negligence claims involving general practitioners.[7]

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.[6] 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).[8]

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.[9]

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.[10]

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.[11] 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[11], 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 records on healthcare[12][13], and the impact of the rapid flu tests on emergency department (ED) performance and patient outcomes.[14]


References

  1. ↑ Knottnerus, J.A.; van Weel, C.; Muris, J.W.M. (2002). "Evaluation of diagnostic procedures". BMJ 324 (7335): 477-80. doi:10.1136/bmj.324.7335.477. PMC PMC1122397. PMID 11859054. https://www.ncbi.nlm.nih.gov/pmc/articles/PMC1122397. 
  2. ↑ Rowlands, D. (2 December 2019). "What is digital health? And why does it matter?" (PDF). Digital Health Workforce Academy. https://www.hisa.org.au/wp-content/uploads/2019/12/What_is_Digital_Health.pdf?x97063. 
  3. ↑ El-Kareh, R.; Hasan, O.; Schiff, G.D. (2013). "Use of health information technology to reduce diagnostic errors". BMJ Quality and 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. 
  4. ↑ Aller, R.D.; Georgiou, A.; Pantanowitz, L. (2012). "Electronic Health Records". In Pantanowitz, L.; Tuthill, J.M.; Balis, U.. Pathology Informatics: Theory and Practice. pp. 217–30. ISBN 9780891896197. 
  5. ↑ Global Antimicrobial Resistance Surveillance System (2016). "Diagnostic stewardship: A guide to implementation in antimicrobial resistance surveillance sites". World Health Organization. https://www.who.int/publications/i/item/WHO-DGO-AMR-2016.3. 
  6. ↑ 6.0 6.1 6.2 Balogh, E.P.; Miller, B.T.; Ball, J.R., ed. (2015). Improving Diagnosis in Health Care. The National Academies Press. doi:10.17226/21794. ISBN 9780309377706. 
  7. ↑ Bird, S. (23 February 2018). "Reducing diagnostic error". newsGP. https://www1.racgp.org.au/newsgp/professional/reducing-diagnostic-error. Retrieved 18 January 2019. 
  8. ↑ Zhi, M.; Ding, E.L.; Theisen-Toupal, J. et al. (2013). "The landscape of inappropriate laboratory testing: A 15-year meta-analysis". PLoS One 8 (11): e78962. doi:10.1371/journal.pone.0078962. PMC PMC3829815. PMID 24260139. https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3829815. 
  9. ↑ Elwenspoek, M.M.C.; Scott, L.J.; Alsop, K. et al. (2020). "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". Family Practice 37 (6): 845-853. doi:10.1093/fampra/cmaa074. PMC PMC7759753. PMID 32820328. https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7759753. 
  10. ↑ Scowen, C.; Wabe, N.; Eigenstetter, A. et al. (2020). "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". BMJ Open 10 (10): e039437. doi:10.1136/bmjopen-2020-039437. PMC PMC7552857. PMID 33046472. https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7552857. 
  11. ↑ 11.0 11.1 Campbell, C.A.; Ling, L.; Kotwal, S. et al. (2020). "Under-detection of acute kidney injury in hospitalised patients: A retrospective, multi-site, longitudinal study". Internal Medicine Journal 50 (3): 307-314. doi:10.1111/imj.14264. PMID 30816607. 
  12. ↑ Georgiou, A.; Vecellio, E.; Toouli, G. et al. (2013). "Monitoring the impact of the electronic medical record on the quality of laboratory test ordering practices". Studies in Health Technologies and Informatics 188: 33–8. PMID 23823285. 
  13. ↑ Template:Cite book url=https://www.health.gov.au/internet/main/publishing.nsf/Content/0E3978777CE9637BCA2580C200088F78/$File/Final Report - Variation in Hospital Pathology - Macquarie University.pdf
  14. ↑ Wabe, N.; Li, L.; Lindeman, R. et al. (2019). "The impact of rapid molecular diagnostic testing for respiratory viruses on outcomes for emergency department patients". Medical Journal of Australia 210 (7): 316–20. doi:10.5694/mja2.50049. PMC PMC6617970. PMID 30838671. https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6617970. 

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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