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1.
J Patient Saf ; 15(2): 77-85, 2019 06.
Article in English | MEDLINE | ID: mdl-26558652

ABSTRACT

BACKGROUND: There is widespread agreement that the full potential of health information technology (health IT) has not yet been realized and of particular concern are the examples of unintended consequences of health IT that detract from the safety of health care or from the use of health IT itself. The goal of this project was to obtain additional information on these health IT-related problems, using a mixed methods (qualitative and quantitative) analysis of electronic health record-related harm in cases submitted to a large database of malpractice suits and claims. METHODS: Cases submitted to the CRICO claims database and coded during 2012 and 2013 were analyzed. A total of 248 cases (<1%) involving health IT were identified and coded using a proprietary taxonomy that identifies user- and system-related sociotechnical factors. Ambulatory care accounted for most of the cases (146 cases). Cases were most typically filed as a result of an error involving medications (31%), diagnosis (28%), or a complication of treatment (31%). More than 80% of cases involved moderate or severe harm, although lethal cases were less likely in cases from ambulatory settings. Etiologic factors spanned all of the sociotechnical dimensions, and many recurring patterns of error were identified. CONCLUSIONS: Adverse events associated with health IT vulnerabilities can cause extensive harm and are encountered across the continuum of health care settings and sociotechnical factors. The recurring patterns provide valuable lessons that both practicing clinicians and health IT developers could use to reduce the risk of harm in the future. The likelihood of harm seems to relate more to a patient's particular situation than to any one class of error.


Subject(s)
Electronic Health Records/standards , Malpractice/trends , Humans , Retrospective Studies , Safety
2.
Jt Comm J Qual Patient Saf ; 43(10): 508-516, 2017 10.
Article in English | MEDLINE | ID: mdl-28942775

ABSTRACT

BACKGROUND: Diagnostic errors are an underrecognized source of patient harm, and cardiovascular disease can be challenging to diagnose in the ambulatory setting. Although malpractice data can inform diagnostic error reduction efforts, no studies have examined outpatient cardiovascular malpractice cases in depth. A study was conducted to examine the characteristics of outpatient cardiovascular malpractice cases brought against general medicine practitioners. METHODS: Some 3,407 closed malpractice claims were analyzed in outpatient general medicine from CRICO Strategies' Comparative Benchmarking System database-the largest detailed database of paid and unpaid malpractice in the world-and multivariate models were created to determine the factors that predicted case outcomes. RESULTS: Among the 153 patients in cardiovascular malpractice cases for whom patient comorbidities were coded, the majority (63%) had at least one traditional cardiac risk factor, such as diabetes, tobacco use, or previous cardiovascular disease. Cardiovascular malpractice cases were more likely to involve an allegation of error in diagnosis (75% vs. 47%, p <0.0001), have high clinical severity (86% vs. 49%, p <0.0001) and result in death (75% vs. 27%, p <0.0001), as compared to noncardiovascular cases. Initial diagnoses of nonspecific chest pain and mimics of cardiovascular pain (for example, esophageal disease) were common and independently increased the likelihood of a claim resulting in a payment (p <0.01). CONCLUSION: Cardiovascular malpractice cases against outpatient general medicine physicians mostly occur in patients with conventional risk factors for coronary artery disease and are often diagnosed with common mimics of cardiovascular pain. These findings suggest that these patients may be high-yield targets for preventing diagnostic errors in the ambulatory setting.


Subject(s)
Cardiovascular Diseases/diagnosis , Cardiovascular Diseases/epidemiology , Diagnostic Errors/statistics & numerical data , Malpractice/statistics & numerical data , Outpatients/statistics & numerical data , Age Factors , Comorbidity , Diabetes Mellitus/epidemiology , Female , Humans , Male , Middle Aged , Retrospective Studies , Risk Factors , Severity of Illness Index , Sex Factors , Smoking/epidemiology , Socioeconomic Factors
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