Literature DB >> 23974542

Is healthcare information technology based on evidence?

R Koppel1.   

Abstract

Is healthcare information technology (HIT) based on evidence of efficacy? Are the trillions of dollars already devoted and in the pipeline for HIT implementations based on systematic evaluations? If evaluated, would those evaluations focus on patient safety, return on investment, clinical efficiency, improved clinician satisfaction, and/or workflow integration? Do we have reliable evidence of usable interfaces, of successful implementations, of data standards allowing interoperability, of continuous improvement, of responsiveness to clinician feedback? While measurement of HIT's efficacy is extraordinarily difficult-complicated by a myriad of other factors involved in providing healthcare and in organizational dynamics-it is not impossible. But is such evidence required before most implementations? Any implementation? Or are the goals of patient safety and efficiency so self-evident, profoundly desired, and laudable that HIT's beneficence is accepted without rigorous data? Note that lack of systematic evidence does not mean HIT is ineffective. HIT may provide untold benefits even if there is no hard proof of those benefits. We find that HIT is seldom objectively measured, and that evidence of its efficacy is at best spotty, and often influenced by self-promotion. Most measures, especially those associated with cost-benefit analyses, are aspirational or hubris transubstantiated into numbers.

Entities:  

Mesh:

Year:  2013        PMID: 23974542

Source DB:  PubMed          Journal:  Yearb Med Inform        ISSN: 0943-4747


  10 in total

Review 1.  Towards Usable E-Health. A Systematic Review of Usability Questionnaires.

Authors:  Vanessa E C Sousa; Karen Dunn Lopez
Journal:  Appl Clin Inform       Date:  2017-05-10       Impact factor: 2.342

2.  Tree testing of hierarchical menu structures for health applications.

Authors:  Thai Le; Shomir Chaudhuri; Jane Chung; Hilaire J Thompson; George Demiris
Journal:  J Biomed Inform       Date:  2014-02-26       Impact factor: 6.317

3.  Effect of a clinical decision support system on early action on immunological treatment failure in patients with HIV in Kenya: a cluster randomised controlled trial.

Authors:  Tom Oluoch; Abraham Katana; Daniel Kwaro; Xenophon Santas; Patrick Langat; Samuel Mwalili; Kimeu Muthusi; Nicky Okeyo; James K Ojwang; Ronald Cornet; Ameen Abu-Hanna; Nicolette de Keizer
Journal:  Lancet HIV       Date:  2015-12-17       Impact factor: 12.767

4.  Reducing Alert Burden in Electronic Health Records: State of the Art Recommendations from Four Health Systems.

Authors:  John D McGreevey; Colleen P Mallozzi; Randa M Perkins; Eric Shelov; Richard Schreiber
Journal:  Appl Clin Inform       Date:  2020-01-01       Impact factor: 2.342

5.  Missing clinical and behavioral health data in a large electronic health record (EHR) system.

Authors:  Jeanne M Madden; Matthew D Lakoma; Donna Rusinak; Christine Y Lu; Stephen B Soumerai
Journal:  J Am Med Inform Assoc       Date:  2016-04-14       Impact factor: 4.497

6.  Looking Behind the Curtain: Identifying Factors Contributing to Changes on Care Outcomes During a Large Commercial EHR Implementation.

Authors:  Tiago K Colicchio; Damian Borbolla; Vanessa D Colicchio; Debra L Scammon; Guilherme Del Fiol; Julio C Facelli; Watson A Bowes; Scott P Narus
Journal:  EGEMS (Wash DC)       Date:  2019-05-06

Review 7.  A review of measurement practice in studies of clinical decision support systems 1998-2017.

Authors:  Philip J Scott; Angela W Brown; Taiwo Adedeji; Jeremy C Wyatt; Andrew Georgiou; Eric L Eisenstein; Charles P Friedman
Journal:  J Am Med Inform Assoc       Date:  2019-10-01       Impact factor: 4.497

8.  Unintended Consequences of Nationwide Electronic Health Record Adoption: Challenges and Opportunities in the Post-Meaningful Use Era.

Authors:  Tiago K Colicchio; James J Cimino; Guilherme Del Fiol
Journal:  J Med Internet Res       Date:  2019-06-03       Impact factor: 5.428

9.  Use of online knowledge base in primary health care and correlation to health care quality: an observational study.

Authors:  Christian Gerdesköld; Eva Toth-Pal; Inger Wårdh; Gunnar H Nilsson; Anna Nager
Journal:  BMC Med Inform Decis Mak       Date:  2020-11-16       Impact factor: 2.796

10.  Exploring the Use of Evidence From the Development and Evaluation of an Electronic Health (eHealth) Trial: Case Study.

Authors:  Monika Jurkeviciute; Henrik Eriksson
Journal:  J Med Internet Res       Date:  2020-08-28       Impact factor: 5.428

  10 in total

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