Advances in Sharing Multi-sourced Health Data on Decision Support Science 2016-2017
29 August 2018 (online)
Introduction: Clinical decision support science is expanding to include integration from broader and more varied data sources, diverse platforms and delivery modalities, and is responding to emerging regulatory guidelines and increased interest from industry.
Objective: Evaluate key advances and challenges of accessing, sharing, and managing data from multiple sources for development and implementation of Clinical Decision Support (CDS) systems in 2016-2017.
Methods: Assessment of literature and scientific conference proceedings, current and pending policy development, and review of commercial applications nationally and internationally.
Results: CDS research is approaching multiple landmark points driven by commercialization interests, emerging regulatory policy, and increased public awareness. However, the availability of patient-related “Big Data” sources from genomics and mobile health, expanded privacy considerations, applications of service-based computational techniques and tools, the emergence of “app” ecosystems, and evolving patient-centric approaches reflect the distributed, complex, and uneven maturity of the CDS landscape. Nonetheless, the field of CDS is yet to mature. The lack of standards and CDS-specific policies from regulatory bodies that address the privacy and safety concerns of data and knowledge sharing to support CDS development may continue to slow down the broad CDS adoption within and across institutions.
Conclusion: Partnerships with Electronic Health Record and commercial CDS vendors, policy makers, standards development agencies, clinicians, and patients are needed to see CDS deployed in the evolving learning health system.
- 1 Osheroff JA, Teich JM, Middleton B, Steen EB, Wright A, Detmer DE. A Roadmap for National Action on Clinical Decision Support. J Am Med Inform Assoc 2007; Mar 1; 14 (02) 141-5
- 2 Osheroff J, Teich J, Levick D, Saldana L, Velasco F, Sittig D. , et al. Improving outcomes with clinical decision support: an implementer's guide. Chicago, IL: Healthcare Information and Management Systems Society (HIMSS); 2012
- 3 Greenes RA. Health Information Systems 2025. In: Weaver CA, Ball MJ, Kim GR, Kiel JM. , editors. Healthcare Information Management Systems: Cases, Strategies, and Solutions. Cham: Spring-er International Publishing; 2016: 579-600 . (Healthcare Information Management Systems: Cases, Strategies, and Solutions).
- 4 Middleton B, Sittig DF, Wright A. Clinical De-cision Support: a 25 Year Retrospective and a 25 Year Vision. Yearb Med Inform 2016; Aug 2; (Suppl. 01) S103-16
- 5 Jenders RA. Advances in Clinical Decision Support: Highlights of Practice and the Liter-ature 2015-2016. Yearb Med Inform 2017; Aug 19; 26 (01) 125-32
- 6 Mandl KD, Mandel JC, Kohane IS. Driving Innovation in Health Systems through an Apps-Based Information Economy. Cell Syst 2015; Jul 29; 1 (01) 8-13
- 7 Clinical and Patient Decision Support Software: Draft Guidance for Industry and Food and Drug Administration Staff. fda.gov Washington, D.C; Dec 8, 2017.
- 8 Fostering Medical Innovation: A Plan for Dig-ital Health Devices; Software Precertification Pilot Program [Internet]. Washington, D.C.: FDA. Available from: https://www.federalreg-ister.gov/documents/2017/07/28/2017-15891/fostering-medical-innovation-a-plan-for-digi-tal-health-devices-software-precertification-pilot
- 9 Voluntary Guidelines for the Design of Clinical Decision Support Software to Assure the Central Role of Healthcare Professionals in Clinical Decision- Making [Internet]. . Washington, DC 20037: CDS Coalition; 2017 Apr. Available from: http://cdscoalition.org/wp-content/uploads/2017/04/CDS-3060-Guidelines-032717-with-memo.pdf
- 10 Labkoff SE, Sittig DF. Who Watches the Watchers. Appl Clin Inform 2017; 8 (02) 680-5
- 11 Health IT Safety Center Roadmap. healthitsafety. org Jul 21, 2015
- 12 2017 Comprehensive Accreditation Manual for Hospitals (CAMH) | Joint Commission Resources. Joint Commission; 2016 Dec 1. Available from: https://www.jcrinc.com/2017-comprehensive-ac-creditation-manuals/2017-comprehensive-accred-itation-manual-for-hospitals-camh-/
- 13 Sow DM. Big Data Analytical Technologies and Decision Support in Critical Care. In: Healthcare Information Management Systems. Cham: Springer; 2016: 515-27 . (Health Informatics).
- 14 Bai Y, Sow D, Vespa P, Hu X. Real-Time Pro-cessing of Continuous Physiological Signals in a Neurocritical Care Unit on a Stream Data Analytics Platform. In: Intracranial Pressure and Brain Monitoring XV. Cham: Springer; 2016: 75-80 . (Acta Neurochirurgica Supplement; vol. 122)
- 15 Adams J, Lieng M, Kuhn B, Guo E, Simonian E. , et al. Automated Mechanical Ventilator Waveform Analysis of Patient-Ventilator Asynchrony. Chest 2015; 148 (04) 175A
- 16 Suresh S. Big Data and Predictive Analytics: Applications in the Care of Children. Pediatric Clin North Am 2016; Apr; 63 (02) 357-66
- 17 Marco-Ruiz L, Pedrinaci C, Maldonado JA, Panziera L, Chen R, Bellika JG. Publication, discovery and interoperability of Clinical Decision Support Systems: A Linked Data approach. J Biomed Inform 2016; Aug; 62: 243-64
- 18 Zini EM, Lanzola G, Bossi P, Quaglini S. An Environment for Guideline-based Decision Support Systems for Outpatients Monitoring. Methods Inf Med 2017; Aug 11; 56 (04) 283-93
- 19 Rodriguez-Loya S, Kawamoto K. Newer Architec-tures for Clinical Decision Support. In: Clinical Decision Support Systems. Cham: Springer; 2016: 87-97 . (Health Informatics; vol. 8).
- 20 Ali T, Hussain M, Ali Khan W, Afzal M, Hussain J, Ali R. , et al. Multi-model-based interactive au-thoring environment for creating shareable medical knowledge. Comput Methods Programs Biomed 2017; Oct; 150: 41-72
- 21 Liu X, Deng R, Choo K-KR, Yang Y. Privacy-Pre-serving Outsourced Clinical Decision Support Sys-tem in the Cloud. IEEE Trans Serv Comput 1-1
- 22 Huang Y-P. The service oriented architecture in decision support system. IEEE 2016; 868-72
- 23 Goldberg HS, Paterno MD, Grundmeier RW, Rocha BH, Hoffman JM, Tham E. , et al. Use of a remote clinical decision support service for a multicenter trial to implement prediction rules for children with minor blunt head trauma. Int J Med Inform 2016; Mar 1; 87: 101-10
- 24 Kuppermann N, Holmes JF, Dayan PS. Head in-jury decision rules in children. Lancet 2017; Sep 23; 390 (10101): 1487-8
- 25 Ohno-Machado L. Using health information technology for clinical decision support and pre-dictive analytics. J Am Med Inform Assoc 2017; Jan 1; 24 (01) 1-1
- 26 OpenCDS Home [Internet]. opencds.org. [cited 2017 Dec 12]. Available from: http://www.opencds.org/
- 27 HL7 Standards Product Brief - HL7 Fast Health-care Interoperability Resources Specification (FHIR®), DSTU Release 1. hl7.org
- 28 Dowell D, Haegerich TM, Chou R. CDC Guideline for Prescribing Opioids for Chronic Pain—United States, 2016. JAMA 2016; Apr 19; 315 (15) 1624
- 29 CQF Home - ONC Tech Lab Standards Coordi-nation - Confluence [Internet]. oncprojectracking. healthit.gov. [cited 2017 Dec 11]. Available from: https://oncprojectracking.healthit.gov/wiki/dis-play/TechLabSC/CQF+Home
- 30 Welcome to openEHR [Internet]. openehr.org. [cited 2017 Dec 11]. Available from: http://www.openehr.org/home
- 31 González-Ferrer A, Peleg M, Marcos M, Maldonado JA. Analysis of the process of representing clinical statements for decision-support applica-tions: a comparison of openEHR archetypes and HL7 virtual medical record. J Med Syst 2016; May 21; 40 (07) 744
- 32 Kane-Gill SL, O’Connor MF, Rothschild JM, Selby NM, McLean B, Bonafide CP. , et al. Technologic Distractions (Part 1): Summary of Approaches to Manage Alert Quantity With Intent to Reduce Alert Fatigue and Suggestions for Alert Fatigue Metrics. Crit Care Med 2017; Sep; 45 (09) 1481-8
- 33 Khalifa M, Zabani I. Improving Utilization of Clinical Decision Support Systems by Reducing Alert Fatigue: Strategies and Recommendations. Stud Health Technol Inform 2016; 226: 51-4
- 34 McCallie DP. Clinical Decision Support: History and Basic Concepts. In: Healthcare Information Management Systems. Cham: Springer; 2016: 3-19 . (Health Informatics).
- 35 Mandel JC, Kreda DA, Mandl KD, Kohane IS, Ramoni RB. SMART on FHIR: a standards-based, interoperable apps platform for electronic health records. J Am Med Inform Assoc 2016; Aug 24; 23 (05) 899-908
- 36 Sinha S, Jensen M, Mullin S, Elkin PL. Safe Opioid Prescription: A SMART on FHIR Approach to Clinical Decision Support. Online J Public Health Inform 2017; 9 (02) e193
- 37 Warner JL, Rioth MJ, Mandl KD, Mandel JC, Kreda DA, Kohane IS. , et al. SMART precision cancer medicine: a FHIR-based app to provide genomic information at the point of care. J Am Med Inform Assoc 2016; Jun 29; 23 (04) 701-10
- 38 Wang Y, Kung L, Byrd TA. Big data analytics: Understanding its capabilities and potential benefits for healthcare organizations. Technological Forecasting and Social Change. North-Holland 2018; Jan 1; 126: 3-13
- 39 Ferrucci D, Levas A, Bagchi S, Gondek D, Mueller ET. . Watson: Beyond Jeopardy! Artif Intell 2013; Jun 1;199-200: 93-105
- 40 Devarakonda MV, Mehta N. Cognitive Computing for Electronic Medical Records. In: Healthcare In-formation Management Systems. Cham: Springer; 2016: 555-77 . (Health Informatics).
- 41 Zhou N, Lv H, Zhang C, Li T, Zhu J, Jiang M. , et al. P1.01-069 Clinical Experience with IBM Watson for Oncology (WFO) Cognitive System for Lung Cancer Treatment in China. Journal of Thoracic Oncology . Elsevier; 2017; Nov 1; 12 (11) S1921
- 42 Chen Y, Elenee Argentinis JD, Weber G. IBM Wat-son: How Cognitive Computing Can Be Applied to Big Data Challenges in Life Sciences Research. Clin Ther 2016; Apr 1; 38 (04) 688-701
- 43 Zhang XC, Zhou N, Zhang CT, Lv HY, Li TJ, Zhu JJ. , et al. 544PConcordance study between IBM Watson for Oncology (WFO) and clinical practice for breast and lung cancer patients in China. Ann Oncol 2017 Nov 1; 28:(suppl_10)
- 44 Aronson SJ, Williams MS. Genetics Aware Clinical Decision Support. In: Genomic and Precision Medicine. Elsevier 2017; 205-15
- 45 Masys DR. Electronic Health Records and Genomic Medicine. In: Genomic and Precision Medicine. Elsevier; 2017. p. 131–42
- 46 Ginsberg G, Willard H. , editors. Genomic and Precision Medicine: Foundations, Translation, and Implementation, 3rd Edition. Academic Press; 2017. 1 p.
- 47 Cutting E, Banchero M, Beitelshees AL, Cimino JJ, Fiol GD, Gurses AP. , et al. User-centered design of multi-gene sequencing panel reports for clinicians. J Biomed Inform 2016; Oct; 63: 1-10
- 48 Klinkenberg-Ramirez S, Neri PM, Volk LA, Samaha SJ, Newmark LP, Pollard S. , et al. Evaluation: A Qualitative Pilot Study of Novel Information Technology Infrastructure to Commu-nicate Genetic Variant Updates. Appl Clin Inform 2016; 7 (02) 461-76
- 49 Heale BSE, Overby CL, Del Fiol G, Rubinstein WS, Maglott DR, Nelson TH. , et al. Integrating Genomic Resources with Electronic Health Re-cords using the HL7 Infobutton Standard. Appl Clin Inform 2016; 7 (03) 817-31
- 50 Manzi SF, Fusaro VA, Chadwick L, Brownstein C, Clinton C, Mandl KD. , et al. Creating a scalable clinical pharmacogenomics service with automated interpretation and medical record result integration – experience from a pediatric tertiary care facility. J Am Med Inform Assoc 2016; Dec 30; 24 (01) 74-80
- 51 Hinderer M, Boeker M, Wagner SA, Lablans M, Newe S, Hülsemann JL. , et al. Integrating clinical decision support systems for pharmacogenomic testing into clinical routine - a scoping review of designs of user-system interactions in recent system development. BMC Med Inform Decis Mak 2017; Jun 6; 17 (01) 450
- 52 Pharmacogenomic Resource for Enhanced Decisions in Care and Treatment (PREDICT) [Internet]. mc.vanderbilt.edu. [cited 17AD Dec 12]. Available from: http://www.mc.vanderbilt.edu/documents/predictpdx/files/PREDICT%20Fact%20Sheet%20for%20Providers%202-15-12.pdf
- 53 Caraballo PJ, Hodge LS, Bielinski SJ, Stewart AK, Farrugia G, Schultz CG. , et al. Multidisciplinary model to implement pharmacogenomics at the point of care. Genet Med 2016; Sep 22; 19 (04) 421-9
- 54 O'Donnell PH, Wadhwa N, Danahey K, Borden BA, Lee SM, Hall JP. , et al. Pharmacogenom-ics-Based Point-of-Care Clinical Decision Support Significantly Alters Drug Prescribing. Clin Phar-macol Ther 2017; Jun 15; 102 (05) 859-69
- 55 Peterson JF, Field JR, Shi Y, Schildcrout JS, Denny JC, McGregor TL. , et al. Attitudes of clinicians following large-scale pharmacogenomics implementation. Pharmacogenomics J 2015; Aug 11; 16 (04) 393-8
- 56 Herr T, Denny J, Hakonarson H, Hartzler A, Hripcsak G, Kannry J. , et al. Practical considerations in genomic decision support: The eMERGE experience. J Pathol Inform 2015; 6 (01) 50
- 57 Freimuth RR, Formea CM, Hoffman JM, Matey E, Peterson JF, Boyce RD. Implementing Ge-nomic Clinical Decision Support for Drug-Based Precision Medicine. CPT Pharmacometrics Syst Pharmacol 2017; Mar 15; 6 (03) 153-5
- 58 Smoller J, Karlson E, Green R, Kathiresan S, MacArthur D, Talkowski M. , et al. An eMERGE Clinical Center at Partners Personalized Medicine. JPM 2016; Dec; 6 (04) 5
- 59 Kart Ö, Mevsim V, Kut A, Yürek İ, Altın AÖ, Yılmaz O. A mobile and web-based clinical decision support and monitoring system for diabetes mel-litus patients in primary care: a study protocol for a randomized controlled trial. BMC Med Inform Decis Mak 2017; Nov 29; 17 (01) 646
- 60 Seto E, Ware P, Logan AG, Cafazzo JA, Chapman KR, Segal P. , et al. Self-Management and Clinical Decision Support for Patients With Complex Chronic Conditions Through the Use of Smartphone-Based Telemonitoring: Randomized Controlled Trial Protocol. JMIR Res Protoc 2017; 6 (11) e229
- 61 Jackson BD, Con D, De Cruz P. Design consid-erations for an eHealth decision support tool in inflammatory bowel disease self-management. Intern Med J 2017 Nov 14
- 62 Guo Y, Chen Y, Lane DA, Liu L, Wang Y, Lip GYH. Mobile Health Technology for Atrial Fibrillation Management Integrating Decision Support, Edu-cation, and Patient Involvement: mAF App Trial. Am J Med 2017; Dec; 130 (12) 1388-1396.e6
- 63 Seaman JB, Arnold RM, Scheunemann LP, White DB. An Integrated Framework for Effective and Efficient Communication with Families in the Adult Intensive Care Unit. Ann Am Thorac Soc 2017; Jun; 14 (06) 1015-20
- 64 Otte-Trojel T, Rundall TG, de Bont A, van de Klundert J, Reed ME. The organizational dynamics enabling patient portal impacts upon organizational performance and patient health: a qualitative study of Kaiser Permanente. BMC Health Serv Res 2nd ed. 2015; Dec 16; 15 (01) 2034
- 65 Fraccaro P, Vigo M, Balatsoukas P, van der Veer SN, Hassan L, Williams R. , et al. Presentation of laboratory test results in patient portals: influence of interface design on risk interpretation and visual search behaviour. BMC Med Inform Decis Mak 2018; Feb 12; 18 (01) 707
- 66 Williams JL, Rahm AK, Zallen DT, Stuckey H, Fultz K, Fan AL. , et al. Impact of a Patient-Facing Enhanced Genomic Results Report to Improve Understanding, Engagement, and Communication. J Genet Counsel 2017; Dec 4; 23 (04) 281
- 67 Beneficiary Engagement and Incentives: Shared Decision Making (SDM) Model | Center for Medi-care & Medicaid Innovation [Internet]. CMS.gov; 2016 Dec. Available from: https://innovation.cms.gov/initiatives/Beneficiary-Engagement-SDM
- 68 Beneficiary Engagement and Incentives Models: Direct Decision Support Model (DDM) Center for Medicare & Medicaid Innovation [Internet]. CMS.gov; 2016 Aug. Available from: https://innovation.cms.gov/initiatives/Beneficiary-En-gagement-DDS/
- 69 Kawamoto K, Anstrom KJ, Anderson JB, Bosworth HB, Lobach DF, McAdam-Marx C. , et al. Long-Term Impact of an Electronic Health Record-En-abled, Team-Based, and Scalable Population Health Strategy Based on the Chronic Care Model. AMIA Annu Symp Proc 2016; 2016: 686-95
- 70 Rajeevan N, Niehoff KM, Charpentier P, Levin FL, Justice A, Brandt CA. , et al. Utilizing patient data from the veterans administration electronic health record to support web-based clinical decision support: informatics challenges and issues from three clinical domains. BMC Med Inform Decis Mak 2017; Jul 19; 17 (01) 135
- 71 Bekemeier B, Park S. Development of the PHAST model: generating standard public health services data and evidence for decision-making. J Am Med Inform Assoc 2017; Nov 2; 95 (06) 930
- 72 Manrai AK, Cui Y, Bushel PR, Hall M, Karakitsios S, Mattingly CJ. , et al. Informatics and Data Ana-lytics to Support Exposome-Based Discovery for Public Health. Annu Rev Public Health 2017; Mar 20; 38 (01) 279-94
- 73 Clinical Decision Support for Immunization (CDSi): Logic Specification for ACIP Recommen-dations. cdc.gov Aug 25, 2017
- 74 What We Do - Adapting Clinical Guidelines for the Digital Age | OPHSS | CDC [Internet]. cdc. gov. [cited 2017 Dec 11]. Available from: https://www.cdc.gov/ophss/WhatWeDoACG.html
- 75 Goodman KW. Ethical and Legal Issues in Deci-sion Support. In: Clinical Decision Support Sys-tems. Cham: Springer; 2016. . pp. 131–46. (Health Informatics; vol. 102).
- 76 GA4GH Strikes Formal Collaborations with 15 International Genomic Data Initiatives [Internet]. ga4gh.org. [cited 2017 Dec 11]. Available from: https://www.ga4gh.org/news/sAhZCeJjS96QHh-VPIYwwWA.article
- 77 Global Alliance for Genomics and Health: Privacyand Security Policy. 26 ed. ga4gh.org. 2015 May
- 78 Framework for responsible sharing genomic and health related data [Internet]. ga4gh.org. 2015. Available from: https://www.ga4gh.org/ga4gh-toolkit/regulatoryandethics/framework-for-re-sponsible-sharing-genomic-and-health-relat-ed-data/#%7B%22-%22:%7B%7D%7D
- 79 Salerno J, Knoppers BM, Lee LM, Hlaing WM, Goodman KW. Ethics, big data and computing in epidemiology and public health. Ann Epidemiol 2017; May; 27 (05) 297-301
- 80 Hollis KF. To Share or Not to Share: Ethical Acquisition and Use of Medical Data. AMIA Jt Summits Transl Sci Proc. Berlin, Heidelberg: American Medical Informatics Association; 2016. ;2016(Chapter 10): 420-7
- 81 Rosenbaum L. Bridging the Data-Sharing Divide — Seeing the Devil in the Details, Not the Other Camp. http://dxdoiorg/101056/NEJMp1704482 . Massachusetts Medical Society; 2017 Apr 26;376(23):NEJMp1704482–2203
- 82 allofus.nih.gov [Internet]. [cited 2017 Dec 13]. Available from: https://allofus.nih.gov
- 83 Million Veteran Program (MVP) [Internet]. re-search.va.gov. [cited 2017 Dec 12]. Available from: https://www.research.va.gov/mvp/
- 84 Verily Life Sciences [Internet]. [cited 2017 Dec 12]. Available from: https://verily.com/projects/precision-medicine/baseline-study/
- 85 Johnson III RJ. A Comprehensive Review of an Electronic Health Record System Soon to Assume Market Ascendancy: EPICÂ®. J Healthc Commun 2016 Sep 23; 1(4).
- 86 Holmgren AJ, Adler-Milstein J, McCullough J. Are all certified EHRs created equal? Assessing the relationship between EHR vendor and hospital meaningful use performance. J Am Med Inform Assoc 2017; Nov 24; 50 (06) 1751
- 87 Wright A, Ai A, Ash J, Wiesen JF, Hickman T-TT, Aaron S. , et al. Clinical decision support alert malfunctions: analysis and empirically derived taxonomy. J Am Med Inform Assoc 2017 Oct 16
- 88 AMA Integrated Health Model Initiative (IHMI) Collaboration Ecosystem [Internet]. [cited 2017 Dec 12]. Available from: https://ama-ihmi.org/groups/ihmi-community
- 89 The Learning Health Care System in America : Health and Medicine Division [Internet]. nation-alacademies.org. [cited 2017 Dec 13]. Available from: http://www.nationalacademies.org/hmd/ctivities/Quality/LearningHealthCare.aspx
- 90 Friedman CP, Rubin JC, Sullivan KJ. Toward an Information Infrastructure for Global Health Improvement. Yearb Med Inform 2017; Aug 19; 26 (01) 16-23
- 91 Lessard L, Michalowski W, Fung-Kee-Fung M, Jones L, Grudniewicz A. Architectural frame-works: defining the structures for implementing learning health systems. Implementation Sci 2017; 12 (01) e63
- 92 Nwaru BI, Friedman C, Halamka J, Sheikh A. Can learning health systems help organisations deliver personalised care?. BMC Med 2017; 15 (01) 12