Reducing Risk with Clinical Decision Support

G. Zuccotti; F.L. Maloney; J. Feblowitz; L. Samal; L. Sato; A. Wright

doi:10.4338/ACI-2014-02-RA-0018

Applied Clinical Informatics, Inhaltsverzeichnis

Appl Clin Inform 2014; 05(03): 746-756
DOI: 10.4338/ACI-2014-02-RA-0018

Research Article

Schattauer GmbH

Reducing Risk with Clinical Decision Support

A Study of Closed Malpractice Claims

Authors

G. Zuccotti

¹Brigham and Women’s Hospital and Harvard Medical School, Boston, MA, USA

²Partners HealthCare, Boston, MA, USA

³CRICO/Risk Management Foundation, Cambridge, MA, USA
F.L. Maloney

²Partners HealthCare, Boston, MA, USA
J. Feblowitz

¹Brigham and Women’s Hospital and Harvard Medical School, Boston, MA, USA
L. Samal

¹Brigham and Women’s Hospital and Harvard Medical School, Boston, MA, USA
L. Sato

³CRICO/Risk Management Foundation, Cambridge, MA, USA
A. Wright

¹Brigham and Women’s Hospital and Harvard Medical School, Boston, MA, USA

²Partners HealthCare, Boston, MA, USA

Abstract

Summary

Objective: Identify clinical opportunities to intervene to prevent a malpractice event and determine the proportion of malpractice claims potentially preventable by clinical decision support (CDS).

Materials and Methods: Cross-sectional review of closed malpractice claims over seven years from one malpractice insurance company and seven hospitals in the Boston area. For each event, clinical opportunities to intervene to avert the malpractice event and the presence or absence of CDS that might have a role in preventing the event, were assigned by a panel of expert raters. Compensation paid out to resolve a claim (indemnity), was associated with each CDS type.

Results: Of the 477 closed malpractice cases, 359 (75.3%) were categorized as substantiated and 195 (54%) had at least one opportunity to intervene. Common opportunities to intervene related to performance of procedure, diagnosis, and fall prevention. We identified at least one CDS type for 63% of substantiated claims. The 41 CDS types identified included clinically significant test result alerting, diagnostic decision support and electronic tracking of instruments. Cases with at least one associated intervention accounted for $40.3 million (58.9%) of indemnity.

Discussion: CDS systems and other forms of health information technology (HIT) are expected to improve quality of care, but their potential to mitigate risk had not previously been quantified. Our results suggest that, in addition to their known benefits for quality and safety, CDS systems within HIT have a potential role in decreasing malpractice payments.

Conclusion: More than half of malpractice events and over $40 million of indemnity were potentially preventable with CDS.

Citation: G. Zuccotti G, Maloney FL, Feblowitz J, Samal L, Sato L, Wright A. Reducing risk with clinical decision support: A study of closed malpractice claims. Appl Clin Inf 2014; 5: 746–756

http://dx.doi.org/10.4338/ACI-2014-02-RA-0018

Keywords

Malpractice - clinical decision support systems - health information technology - electronic health records - professional liability

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Referenzen

References
1 McDonald CJ. Protocol-based computer reminders, the quality of care and the non-perfectability of man. N Engl J Med 1976; 295 (24) 1351-1355.
2 Hunt DL, Haynes RB, Hanna SE, Smith K. Effects of computer-based clinical decision support systems on physician performance and patient outcomes: a systematic review. JAMA 1998; 280 (15) 1339-1346.
3 Garg AX, Adhikari NK, McDonald H, Rosas-Arellano MP, Devereaux PJ, Beyene J, Sam J, Haynes RB. Effects of computerized clinical decision support systems on practitioner performance and patient outcomes: a systematic review. JAMA 2005; 293 (10) 1223-1238.
4 Bates DW, Pappius E, Kuperman GJ, Sittig D, Burstin H, Fairchild D, Brennan TA, Teich JM. Using information systems to measure and improve quality. Int J Med Inform 1999; 53 2–3 115-124.
5 Kuperman GJ, Teich JM, Gandhi TK, Bates DW. Patient safety and computerized medication ordering at Brigham and Women’s Hospital. Jt Comm J Qual Improv 2001; 27 (10) 509-521.
6 Bates DW, Teich JM, Lee J, Seger D, Kuperman GJ, Ma’Luf N, Boyle D, Leape L. The impact of computerized physician order entry on medication error prevention. J Am Med Inform Assoc 1999; 6 (04) 313-321.
7 Teich JM, Merchia PR, Schmiz JL, Kuperman GJ, Spurr CD, Bates DW. Effects of computerized physician order entry on prescribing practices. Arch Intern Med 2000; 160 (18) 2741-2747.
8 Jena AB, Seabury S, Lakdawalla D, Chandra A. Malpractice risk according to physician specialty. N Engl J Med 2011; 365 (07) 629-636.
9 Bishop TF, Ryan AM, Casalino LP. Paid malpractice claims for adverse events in inpatient and outpatient settings. JAMA 2011; 305 (23) 2427-2431.
10 Gandhi TK, Kachalia A, Thomas EJ, Puopolo AL, Yoon C, Brennan TA, Studdert DM. Missed and delayed diagnoses in the ambulatory setting: a study of closed malpractice claims. Ann Intern Med 2006; 145 (07) 488-496.
11 Croskerry P. The importance of cognitive errors in diagnosis and strategies to minimize them. Acad Med 2003; 78 (08) 775-780.
12 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; 14 (02) 141-145.
13 Chaudhry B, Wang J, Wu S, Maglione M, Mojica W, Roth E, Morton SC, Shekelle PG. Systematic review: impact of health information technology on quality, efficiency, and costs of medical care. Ann Intern Med 2006; 144 (10) 742-752.
14 Wright A, Goldberg H, Hongsermeier T, Middleton B. A description and functional taxonomy of rule-based decision support content at a large integrated delivery network. J Am Med Inform Assoc 2007; 14 (04) 89-96.
15 Wright A, Sittig DF, Ash JS, Feblowitz J, Meltzer S, McMullen C, Guappone K, Carpenter J, Richardson J, Simonaitis L, Evans RS, Nichol WP, Middelton B. Development and evaluation of a comprehensive clinical decision support taxonomy: comparison of front-end tools in commercial and internally developed electronic health record systems. J Am Med Inform Assoc 2011; 18 (03) 232-242.
16 Zapka J, Taplin SH, Price RA, Cranos C, Yabroff R. Factors in quality care--the case of follow-up to abnormal cancer screening tests--problems in the steps and interfaces of care. J Natl Cancer Inst Monogr 2010; (40) 58-71.
17 Graber ML, Mathew A. Performance of a web-based clinical diagnosis support system for internists. J Gen Intern Med 2008; 23 (Suppl. 01) 37-40.
18 Friedman CP, Elstein AS, Wolf FM, Murphy GC, Franz TM, Heckerling PS, Fine PL, Miller TM, Abraham V. Enhancement of clinicians’ diagnostic reasoning by computer-based consultation: a multisite study of 2 systems. JAMA 1999; 282 (Suppl. 19) 1851-1856.
19 Elkin P, Liebow M, Bauer B, Chailiki S, Wahner-Roedler D, Bundrick J, Lee M, Brown SH, Froehling D, Bailey K, Famiglietti K, Kim R, Hoffer E, Feldman M, Barnett GO. The introduction of a diagnostic decision support system (DXplain™) into the workflow of a teaching hospital service can decrease the cost of service for diagnostically challenging Diagnostic Related Groups (DRGs). International Journal of Medical Informatics 2010; 79 (11) 772-777.
20 Berner E. Diagnostic Decision Support Systems: Why Aren’t They Used More And What Can We Do About It? Proceedings of AMIA Annual Symposium; 2006 Nov 11–15;. Washington DC:
21 Berkowitz S, Marshall H, Charles A. Retained intra-abdominal surgical instruments: time to use nascent technology?. Am Surg 2007; 73 (11) 1083-1085.
22 Greenberg CC, Diaz-Flores R, Lipsitz SR, Regenbogen SE, Mulholland L, Mearn F, Rao S, Toidze T, Gawande AA. Bar-coding surgical sponges to improve safety: a randomized controlled trial. Ann Surg 2008; 247 (04) 612-616.
23 Macario A, Morris D, Morris S. Initial clinical evaluation of a handheld device for detecting retained surgical gauze sponges using radiofrequency identification technology. Arch Surg 2006; 141 (07) 659-662.
24 Stawicki SP, Evans DC, Cipolla J, Seamon MJ, Lukaszczyk JJ, Prosciak MP, Torigian DA, Doraiswany VA, Yazzie NP, Gunter Jr OL, Steinberg SM. Retained surgical foreign bodies: a comprehensive review of risks and preventive strategies. Scand J Surg 2009; 98 (01) 8-17.
25 Studdert DM, Brennan TA, Thomas EJ. Beyond Dead Reckoning: measure of medical injury burden, malpractice litigation and alternative compensation models from Utah and Colorado. Indiana Law Review 2000; 33: 1643-1686.
26 Localio AR, Lawthers AG, Brennan TA, Laird NM, Hebert LE, Peterson LM, Newhouse JP, Weiler PC, Hiatt HH. Relation between malpractice claims and adverse events due to negligence. Results of the Harvard Medical Practice Study III. N Engl J Med 1991; 325 (04) 245-251.
27 Brennan TA, Sox CM, Burstin HR. Relation between negligent adverse events and the outcomes of medical-malpractice litigation. N Engl J Med 1996; 335 (26) 1963-1967.
28 Denham CR, Sullenberger CB, Quaid DW, Nance JJ. An NTSB for Health Care –Learning From Innovation: Debate and Innovate or Capitulate. J Patient Saf 2012; 8 (01) 3-14.
29 Burstin HR, Johnson WG, Lipsitz SR, Brennan TA. Do the poor sue more? A case-control study of malpractice claims and socioeconomic status. JAMA 1993; 270 (14) 1697-1701.
30 Studdert DM, Thomas EJ, Burstin HR, Zbar BI, Orav EJ, Brennan TA. Negligent care and malpractice claiming behavior in Utah and Colorado. Med Care 2000; 38 (03) 250-260.

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