Appl Clin Inform 2019; 10(02): 272-277
DOI: 10.1055/s-0039-1685221
Letter to the Editor
Georg Thieme Verlag KG Stuttgart · New York

Quality Informatics: The Convergence of Healthcare Data, Analytics, and Clinical Excellence

Nathan A. Coppersmith
1   Warren Alpert Medical School and Center for Biomedical Informatics, Brown University, Rhode Island, United States
Indra Neil Sarkar
1   Warren Alpert Medical School and Center for Biomedical Informatics, Brown University, Rhode Island, United States
Elizabeth S. Chen
1   Warren Alpert Medical School and Center for Biomedical Informatics, Brown University, Rhode Island, United States
› Author Affiliations
Funding This work was supported in part by the Brown Center for Biomedical Informatics and Institutional Development Award Number U54GM115677 from the National Institute of General Medical Sciences of the National Institutes of Health, which funds Advance Clinical and Translational Research (Advance-CTR). The content is solely the responsibility of the authors and does not necessarily represent the official views of the National Institutes of Health.
Further Information

Publication History

13 September 2018

06 March 2019

Publication Date:
24 April 2019 (online)

Background and Significance

The convergence of electronic health record (EHR) adoption, increased availability of electronic health data, development of analytical techniques, and healthcare payment reform has created an environment ripe for using information technology to improve delivery of healthcare services.[1] [2] [3] The lack of unification has manifested itself in EHR systems used by providers, quality measures developed by different groups, and use of quality measures in different ways by different organizations.[4] The organization and coordination of formal approaches for using health information systems and their data to improve clinical effectiveness is needed.

Quality informatics is defined here as the discipline that “uses healthcare data collected from healthcare information systems to improve clinical effectiveness.[5]” As part of clinical informatics that broadly studies the organization and use of information to improve healthcare delivery,[6] [7] quality informatics is rooted in collecting accurate healthcare quality data, analyzing those data, and applying findings toward quality improvement programs ([Fig. 1]). Coordination of this work allows clinicians, analysts, and researchers to unify, align, and share in the work of connecting large-scale data with improvement in healthcare delivery. These efforts complement and contribute to parallel efforts to using EHR data for clinical decision support, research, and public health.[8] [9] [10]

Zoom Image
Fig. 1 The work, tools, challenges, and opportunities of quality informatics.

This letter outlines why quality informatics should be recognized, provides an overview of the work of quality informatics, and describes the opportunities and challenges facing the work. Electronic clinical quality measures (eCQMs), part of the Centers for Medicare and Medicaid Services (CMS) Promoting Interoperability EHR Incentive Program, will serve as an exemplar of quality informatics in practice.[11]

Protection of Human and Animal Subjects

Human and/or animal subjects were not included in the project.

  • References

  • 1 Kruse CS, Kothman K, Anerobi K, Abanaka L. Adoption factors of the electronic health record: a systematic review. JMIR Med Inform 2016; 4 (02) e19
  • 2 Weiner JP, Fowles JB, Chan KS. New paradigms for measuring clinical performance using electronic health records. Int J Qual Health Care 2012; 24 (03) 200-205
  • 3 Simpao AF, Ahumada LM, Gálvez JA, Rehman MA. A review of analytics and clinical informatics in health care. J Med Syst 2014; 38 (04) 45
  • 4 Keswani A, Uhler LM, Bozic KJ. What quality metrics is my hospital being evaluated on and what are the consequences?. J Arthroplasty 2016; 31 (06) 1139-1143
  • 5 OHSU Clinfowiki. Quality informatics. Available at: . Accessed June, 2016
  • 6 Kulikowski CA, Shortliffe EH, Currie LM. , et al. AMIA Board white paper: definition of biomedical informatics and specification of core competencies for graduate education in the discipline. J Am Med Inform Assoc 2012; 19 (06) 931-938
  • 7 AMIA. Clinical Informatics. Available at: . Accessed June, 2016
  • 8 Safran C, Bloomrosen M, Hammond WE. , et al; Expert Panel. Toward a national framework for the secondary use of health data: an American Medical Informatics Association White Paper. J Am Med Inform Assoc 2007; 14 (01) 1-9
  • 9 Elkin PL, Trusko BE, Koppel R. , et al. Secondary use of clinical data. Stud Health Technol Inform 2010; 155: 14-29
  • 10 Hripcsak G, Bloomrosen M, FlatelyBrennan P. , et al. Health data use, stewardship, and governance: ongoing gaps and challenges: a report from AMIA's 2012 Health Policy Meeting. J Am Med Inform Assoc 2014; 21 (02) 204-211
  • 11 Peterson KJ, Pathak J. Scalable and high-throughput execution of clinical quality measures from electronic health records using MapReduce and the JBoss® drools engine. AMIA Annu Symp Proc 2014; 2014: 1864-1873
  • 12 CMS proposes changes to empower patients and reduce administrative burden. Available at: . Accessed October 27, 2018
  • 13 Dixon-Woods M, Redwood S, Leslie M, Minion J, Martin GP, Coleman JJ. Improving quality and safety of care using “technovigilance”: an ethnographic case study of secondary use of data from an electronic prescribing and decision support system. Milbank Q 2013; 91 (03) 424-454
  • 14 Mo H, Pacheco JA, Rasmussen LV. , et al. A prototype for executable and portable electronic clinical quality measures using the KNIME analytics platform. AMIA Jt Summits Transl Sci Proc 2015; 2015: 127-131
  • 15 Niland JC, Rouse L, Stahl DC. An informatics blueprint for healthcare quality information systems. J Am Med Inform Assoc 2006; 13 (04) 402-417
  • 16 Millery M, Kukafka R. Health information technology and quality of health care: strategies for reducing disparities in underresourced settings. Med Care Res Rev 2010; 67 (5, Suppl): 268S-298S
  • 17 de Lusignan S. Informatics as tool for quality improvement: rapid implementation of guidance for the management of chronic kidney disease in England as an exemplar. Healthc Inform Res 2013; 19 (01) 9-15
  • 18 Baro E, Degoul S, Beuscart R, Chazard E. Toward a literature-driven definition of big data in healthcare. BioMed Res Int 2015; 2015: 639021
  • 19 Luo J, Wu M, Gopukumar D, Zhao Y. Big data application in biomedical research and health care: a literature review. Biomed Inform Insights 2016; 8: 1-10
  • 20 Janke AT, Overbeek DL, Kocher KE, Levy PD. Exploring the potential of predictive analytics and big data in emergency care. Ann Emerg Med 2016; 67 (02) 227-236
  • 21 Stone-Griffith S, Englebright JD, Cheung D, Korwek KM, Perlin JB. Data-driven process and operational improvement in the emergency department: the ED Dashboard and Reporting Application. J Healthc Manag 2012; 57 (03) 167-180
  • 22 Shaw SJ, Jacobs B, Stockwell DC, Futterman C, Spaeder MC. Effect of a real time pediatric ICU safety bundle dashboard on quality improvement measures. Jt Comm J Qual Patient Saf 2015; 41 (09) 414-420
  • 23 Sukumar SR, Natarajan R, Ferrell RK. Quality of big data in health care. Int J Health Care Qual Assur 2015; 28 (06) 621-634
  • 24 Weiskopf NG, Weng C. Methods and dimensions of electronic health record data quality assessment: enabling reuse for clinical research. J Am Med Inform Assoc 2013; 20 (01) 144-151
  • 25 Weiskopf NG, Hripcsak G, Swaminathan S, Weng C. Defining and measuring completeness of electronic health records for secondary use. J Biomed Inform 2013; 46 (05) 830-836
  • 26 Rusanov A, Weiskopf NG, Wang S, Weng C. Hidden in plain sight: bias towards sick patients when sampling patients with sufficient electronic health record data for research. BMC Med Inform Decis Mak 2014; 14: 51
  • 27 Weiskopf NG, Bakken S, Hripcsak G, Weng C. A data quality assessment guideline for electronic health record data reuse. EGEMS (Wash DC) 2017; 5 (01) 14
  • 28 Callahan TJ, Bauck AE, Bertoch D. , et al. A comparison of data quality assessment checks in six data sharing networks. EGEMS (Wash DC) 2017; 5 (01) 8
  • 29 Lee K, Weiskopf N, Pathak J. A framework for data quality assessment in clinical research datasets. AMIA Annu Symp Proc 2018; 2017: 1080-1089
  • 30 Kahn MG, Callahan TJ, Barnard J. , et al. A harmonized data quality assessment terminology and framework for the secondary use of electronic health record data. EGEMS (Wash DC) 2016; 4 (01) 1244
  • 31 Weigel FK, Switaj TL, Hamilton J. Leveraging health information technology to improve quality in federal healthcare. US Army Med Dep J 2015; ;(Oct–Dec): 68-74
  • 32 Fiscal year (FY) 2019 Medicare hospital inpatient prospective payment system (IPPS) and long term acute care hospital (LTCH) prospective payment system proposed rule, and request for information. Available at: . Accessed November 2, 2018
  • 33 CMS launches data element library supporting interoperability. Available at: . Accessed November 2, 2018
  • 34 Anuradha J. A brief introduction on big data 5Vs characteristics and Hadoop technology. Procedia Comput Sci 2015; 48: 319-324
  • 35 Brown SH, Speroff T, Fielstein EM. , et al. eQuality: electronic quality assessment from narrative clinical reports. Mayo Clin Proc 2006; 81 (11) 1472-1481
  • 36 Brown SH, Elkin PL, Rosenbloom ST, Fielstein E, Speroff T. eQuality for all: extending automated quality measurement of free text clinical narratives. AMIA Annu Symp Proc 2008; 2008: 71-75
  • 37 Murff HJ, FitzHenry F, Matheny ME. , et al. Automated identification of postoperative complications within an electronic medical record using natural language processing. JAMA 2011; 306 (08) 848-855
  • 38 Hazlehurst B, McBurnie M, Mularski R, Puro J, Chauvie S. Automating quality measurement: a system for scalable, comprehensive, and routine care quality assessment. AMIA Annu Symp Proc 2009; 2009: 229-233
  • 39 Hazelhurst B, McBurnie MA, Mularski RA, Puro JE, Chauvie SL. Automating care quality measurement with health information technology. Am J Manag Care 2012; 18 (06) 313-319
  • 40 Burstin H, Leatherman S, Goldmann D. The evolution of healthcare quality measurement in the United States. J Intern Med 2016; 279 (02) 154-159
  • 41 Knoppers BM, Thorogood AM. Ethics and big data in health. Curr Opin Syst Biol 2017; 4: 53-57
  • 42 Cohen IG, Amarasingham R, Shah A, Xie B, Lo B. The legal and ethical concerns that arise from using complex predictive analytics in health care. Health Aff (Millwood) 2014; 33 (07) 1139-1147
  • 43 Tang PC, Ralston M, Arrigotti MF, Qureshi L, Graham J. Comparison of methodologies for calculating quality measures based on administrative data versus clinical data from an electronic health record system: implications for performance measures. J Am Med Inform Assoc 2007; 14 (01) 10-15
  • 44 Rudin RS, Tang PC, Bates DW. Health information technology policy. In: Shortliffe E, Cimino J. , eds. Biomedical Informatics. London: Springer; 2014