1) We are living in the data mining age. Provide an example on how data mining can turn a large collection of data into knowledge that can help meet a current global challenge in order to improve heal

Arch Intern Med. 2008;168(21):2362-2367 I N THE PAST 10 YEARS ,HEALTH IN - formation technology (HIT) has emerged as an essential compo- nent of a transformed health care system that focuses on safety, qual- ity, and efficiency. 1,2 Although results of some studies have been equivocal, 3,4the po- tential impact of HIT on the safe practice of medicine seems increasingly compel- ling: if used actively by caregivers, studies indicate that HIT can reduce adverse drug events and improve physician perfor- mance in areas such as diagnosis, preven- tive care, disease management, drug dos- ing, and drug management.

5,6 One component of HIT in particular, elec- tronic health records (EHRs), has been tar- geted by policymakers as an essential tool for ensuring the secure availability of pa- tient health records across health care en- tities and for reducing health care spend- ing. 7Many clinicians have also recognized the benefits of implementing an EHR de- spite the large initial capital expenditure.

Research indicates that EHRs can improvedocumentation, enhance the efficiency of clinic visits, 8minimize medication errors, and enable clinicians to perform popula- tion surveillance and monitoring. 2,9As a re- sult, EHRs are being increasingly adopted by caregivers seeking to improve the qual- ity of patient care. 10 The potential for EHRs to prevent ad- verse events and reduce health care costs has also created interest in whether use of EHRs reduces the risk of malpractice law- suits. The Joint Commission on Accredi- tation of Healthcare Organizations has sug- gested that HIT can address factors that have proved to be risk points for error and subsequent malpractice suits by patients, such as communication among care- givers, availability of patient informa- tion, medication prescribing, and adher- ence to clinical guidelines. 11One study 12 that involved 307 closed malpractice cases claiming medical negligence found that more than half of the cases were due to di- agnostic errors that harmed patients. Most of these errors occurred because of fail- Author Affiliations:Division of General Medicine and Primary Care, Department of Medicine, Brigham and Women’s Hospital (Drs Virapongse, Bates, and Sato and Ms Jenter), Department of Ambulatory Care and Prevention, Harvard Medical School and Harvard Pilgrim Health Care (Ms Shi and Drs Kleinman and Simon), Boston, Partners Health Care, Wellesley (Dr Bates and Ms Volk), Harvard Risk Management Foundation, Cambridge (Dr Sato), Massachusetts. (REPRINTED) ARCH INTERN MED/ VOL 168 (NO. 21), NOV 24, 2008 WWW. ARCHINTERNMED.COM 2362 ©2008 American Medical Association. All rights reserved.Downloaded From: https://jamanetwork.com/ on 05/30/2020 ure to order diagnostic tests or lack of a follow-up plan.

Because EHRs and HIT seem to mitigate reliance on cog- nitive factors through clinical decision support and avoid- ance of errors of omission, diagnostic errors may in turn decrease with implementation of such systems. Further- more, electronic documentation tends to be superior to the paper record in legibility and completeness. Since many lawsuits hinge on the presentation of proper docu- mentation to the court, a thorough and accurate medi- cal record would likely make lawsuits easier to defend for physicians. 13Many malpractice claims also base their allegations on the failure to adhere to the standard of care.

With the inclusion of decision support into an EHR, phy- sicians can be presented with the relevant guidelines from the onset of ordering treatment and may be more likely to adhere to them.

In addition, malpractice claims due to medical errors constitute the bulk of malpractice claim payouts and ad- ministrative costs.

14Of all malpractice claims, 83% show no evidence of negligence, and most of these claims with- out injury are uncompensated or account for a small frac- tion of overall malpractice costs. 14,15 Thus, if medical er- rors were minimized through HIT, significant health care savings would occur through a reduction in tort- associated costs. Conversely, some studies 16,17 have shown that HIT has the potential to increase adverse events at- tributable to information errors and human-machine in- terface flaws. Although these reports primarily focus on computerized physician order entry systems in hospital settings, the fact remains that adoption of any HIT is not without risk, and unintended consequences may create a new realm of litigation issues.

Despite a considerable body of evidence indicating that HIT can prevent medical errors, little is known about the relationship between EHR adoption in the office prac- tice setting and medical malpractice claims. Few data are available to evaluate the association between use level of EHR functions and the prevalence of malpractice claims.

In the inpatient setting, use of computerized physician order entry was correlated with a lower frequency of medi- cation-related malpractice claims, 18but the frequency of these claims is low enough to make such analyses diffi- cult. To assess whether EHR use was associated with fewer paid malpractice claims, we linked survey data about EHR adoption and use to physician profile data from the Mas- sachusetts Board of Registration in Medicine (BRM). METHODS The sampling methods, survey questionnaire development, and survey administration have been published elsewhere 19,20 and are described briefly herein. SAMPLE Using a database from a private vendor (Folio Associates, Hy- annis, Massachusetts) and information from the BRM, 21we iden- tified the population of practicing physicians in Massachu- setts in 2005. After excluding physicians who were residents in training, retired, or without direct patient-care responsibili- ties, the total population of physicians was 20 227. These phy- sicians practiced in 6174 unique practice sites in Massachu-setts. Of these practices, a stratified random sample of 1921 practices was obtained, and 1 physician from each practice was randomly selected for the survey. After excluding practices that had closed, the final sample size was 1884 physicians. SURVEY We administered a survey by mail between June 1, 2005, and November 30, 2005, to physicians in office practice in Massa- chusetts. The 8-page questionnaire was based on a systematic review of the literature regarding barriers to EHR adoption and ascertained physician and practice characteristics, adoption of EHRs and other HIT, and use of EHR functions. Initially, the sur- vey was sent via express mail with a $20 cash honorarium. Two subsequent mailings to nonresponders were sent without remu- neration. Between mailings, multiple telephone contacts were at- tempted to remind physicians to complete the survey.

The survey ascertained physicians’ personal demographic and practice characteristics and their use of HIT, including EHRs.

Physicians reported their age; race, which we dichotomized as white vs other; year of medical school graduation; and num- ber of physicians in their practice. We determined each phy- sician’s specialty from the database from which we drew the survey sample. MALPRACTICE CLAIMS DATA COLLECTION In April 2007, available identifying data (name, date of gradu- ation, and zip code) were used to access each survey respon- dent’s physician profile on the BRM Web site (http://profiles .massmedboard.org/MA-Physician-Profile-Find-Doctor.asp). The BRM Web site contains information only for the previous 10 years of the physician’s practice. Two trained data extractors (including A.V.), blinded to the physicians’ responses to the survey questionnaire and the specialties of the physicians, in- dependently determined the presence or absence of a paid mal- practice claim for each study physician from the BRM Web site.

If a paid malpractice claim was present, then number of claims and year of the settlement payment was noted.

Data collection sheets from the 2 data extractors were com- pared for accuracy, and any discrepancies were adjudicated using the BRM Web site. After a master data extraction form was com- piled, the names and addresses of the respondents were re- moved and pertinent measures from the survey were merged.

The study protocol was approved by the Partners HealthCare Human Research Committee. STATISTICAL ANALYSIS Statistical analysis was performed using commercially avail- able software programs (Stata Intercooled 9; StataCorp, Col- lege Station, Texas; and SAS statistical software, version 9.1; SAS Institute Inc, Cary, North Carolina). Baseline character- istics between respondents who were EHR adopters and non- adopters, as well as between physicians with and without paid malpractice claims, were compared using the Pearson 2test, the Wilcoxon rank sum test, and the unpaired, 2-tailedttest.

The primary outcome, the presence or absence of paid mal- practice claims among physicians using EHRs and those not using EHRs, was assessed using the Pearson 2and Fisher ex- act test, as appropriate, and calculating unadjusted odds ratios (ORs) with 95% confidence intervals (CIs).

We used logistic regression to adjust for the potential in- fluence of physician characteristics on the relationship be- tween EHR and malpractice claims. The model was run first with all covariates and then with inclusion only of those vari- ables found to be statistically significantly associated (P .05) (REPRINTED) ARCH INTERN MED/ VOL 168 (NO. 21), NOV 24, 2008 WWW. ARCHINTERNMED.COM 2363 ©2008 American Medical Association. All rights reserved.Downloaded From: https://jamanetwork.com/ on 05/30/2020 with paid malpractice claims in bivariate analysis. Because age and graduation year were highly correlated, only graduation year (a proxy for years in practice) was used in the logistic re- gression models. In an exploratory analysis to address the po- tential temporal relationship between EHR adoption and the prevention of malpractice settlements, we excluded any phy- sicians who had paid malpractice claims the date of which pre- ceded the date of EHR adoption. In this analysis, we also ex- cluded any physicians who had adopted EHRs after 2001 based on the assumption that it would take a minimum of 5 years for a malpractice event to result in a paid settlement.

A subsequent analysis limited to EHR adopters examined the relationship between use of EHR functions and paid mal- practice claims. Physicians with EHRs were asked to docu- ment the availability and degree of use of 10 key functions in their EHR. Those who used half or more of their available func- tions all or most of the time were considered “high EHR us- ers,” whereas the remaining physicians were classified as “low users.” 20The rate of paid malpractice claims among high and low users was compared using the 2test.

To determine whether the relationship between EHR adop- tion and paid malpractice claims was similar among physicians in specialties considered high risk vs low risk for malpractice claims, we first determined the percentage of physicians with paid malpractice claims in each specialty within our data set. The per- centages ranged from 0% (dermatology) to 34.6% (general sur- gery). We dichotomized the sample at the median (10.5%) to create a variable that indicated whether each physician prac- ticed in a low-risk or high-risk specialty. For example, internal medicine (7.1%) and family medicine (10.5%) were considered in the low-risk group, whereas obstetrics and gynecology (24.2%) and urology (30.8%) were in the high-risk group. We then ex- amined the relationship between the presence of EHR and paid malpractice settlements within each stratum. RESULTS As reported previously, 19,20 1345 physicians completed the survey (response rate, 71.4%). We excluded 157 phy- sicians who indicated that they did not see outpatientsand 41 physicians who did not have physician profiles on the BRM Web site ( Figure ). Seven physicians did not answer survey questions regarding use of EHRs. This re- sulted in 1140 respondents eligible for analysis.

EHR ADOPTION Overall, 33.2% of the sample (379 of 1140) used EHRs in their practices ( Table 1 ). Physicians who used EHRs were younger than those who did not use EHRs (mean age, 49.1 vs 52.8 years;P .001) and had completed medi- cal school more recently (median graduation year, 1987 vs 1983;P .001). The EHR adopters were less likely to be in solo practice (14.2% vs 35.9%;P .001). Among physicians who used EHRs, 71.8% reported implement- ing their systems within the 10 years preceding the sur- vey. Duration of EHR use ranged from less than 1 year to 18 years among survey respondents who used EHRs in their practice.

PAID MALPRACTICE CLAIMS A total of 105 of the 1140 survey respondents (9.2%) had a history of 1 or more malpractice payments within the past 10 years ( Table 2 ). Paid malpractice claims were more common among male physicians (11.1%) than fe- male physicians (5.6%) (P= .003). Paid malpractice claims were more common among physicians who had been in practice longer. For example, 15.2% of physicians who graduated from medical school more than 20 years ago had paid malpractice claims in the past 10 years com- pared with 5.8% of physicians who had graduated within the past 20 years (P .001) (data not shown). Practice size was also correlated with malpractice claims. Paid mal- practice claims were more common among physicians in solo practice (43.7%) and among those in small group practices of 2 to 4 people (29.1%) and 5 to 9 people (19.4%) than among physicians who practiced in groups of 10 or more physicians (7.8%). Respondents for matching on BRM Web site 1188 Physicians were sent initial survey 1884 Did not answer EHR questions on survey 7 Excluded because of no BRM physician profile 41 Excluded because they reported not seeing outpatients 157 Physicians did not respond 539 Respondents remaining 1147 Survey respondents 1345 Respondents remaining for analysis 1140 Figure.Flow diagram of included and excluded survey respondents. BRM indicates Board of Registration in Medicine; EHR, electronic health record. Table 1. Characteristics of EHR Adopters and Nonadopters a CharacteristicEHR Adopters (n = 379)EHR Nonadopters (n = 761)PValue Age, mean (SD), y 49.1 (9.6) 52.8 (10.7) .001 Women 133 (35.7) 224 (29.9) .05 White race 308 (84.9) 619 (84.9) .98 Median year of medical school graduation (IQR)1987 (1980-1993) 1983 (1974-1991) .001 Practice size .001 Solo practice 53 (14.2) 268 (35.9) 2-4 Physicians 71 (19.0) 268 (35.9) 5-9 Physicians 110 (29.5) 131 (17.5) 10 Physicians 139 (36.3) 80 (10.7) Primary care b 149 (40.2) 297 (39.5) .83 Abbreviations: EHR, electronic health record; IQR, interquartile range. aData are presented as number (percentage) of study participants unless otherwise indicated. Categories do not sum to 1140 because of participant nonresponse; denominators vary for the same reason.

bPrimary care included family practice, general internal medicine, general pediatrics, combined medicine and pediatrics, and geriatrics. (REPRINTED) ARCH INTERN MED/ VOL 168 (NO. 21), NOV 24, 2008 WWW. ARCHINTERNMED.COM 2364 ©2008 American Medical Association. All rights reserved.Downloaded From: https://jamanetwork.com/ on 05/30/2020 Among physicians who used EHRs, 6.1% had a rec- ord of paid malpractice claims compared with 10.8% of physicians who did not use EHRs (unadjusted OR, 0.54; 95% CI, 0.33-0.86;P= .01) (Table 2). In logistic regres- sion analysis controlling for physician sex, race, year of medical school graduation, specialty, and practice size, the relationship between EHR adoption and paid mal- practice settlements was of smaller magnitude and no longer statistically significant (adjusted OR, 0.69; 95% CI, 0.40-1.20;P= .18) ( Table 3 ). A more parsimonious model that adjusted only for variables found to be asso- ciated with the outcome variable demonstrated a rela- tionship between EHR adoption and paid malpractice claims (OR, 0.68; 95% CI, 0.40-1.16;P= .16) that did not materially differ from the fully adjusted model.

In the exploratory analysis that excluded physicians who had adopted EHRs after 2001 and those with paid malpractice settlements the date of which preceded the EHR adoption date, the resultant sample was limited to 117 EHR adopters, of whom 2 (1.7%) had paid malprac- tice settlements. In logistic regression analysis, control- ling for physician sex, year of medical school gradua- tion, and practice size, a significant association was found, indicating that physicians with EHRs were less likely to have paid malpractice claims (adjusted OR, 0.19; 95% CI, 0.05-0.78). The power for this analysis was ex- tremely small because of the small number of outcomes in EHR adopters, and excluding subjects from this group in a nonrandom manner may have led to a more biased result.

Within the physician group that used EHRs, 299 phy- sicians were characterized as high users and 33 as low users. Seventeen of the high users (5.7%) had paid mal- practice claims compared with 4 of the low users (12.1%) (P= .14). Among the 105 physicians with any paid mal- practice claims, 16 had multiple paid claims during the observation period, 3 of whom had EHRs. This preva- lence of EHR adoption among physicians with multiple claims (3 of 16 physicians [18.8%]) was similar to that among those with only 1 paid claim (20 of 89 [22.5%]) (P= .74). In stratified analyses, the relationship between the presence of EHR and paid malpractice claims was simi- lar among physicians practicing in high-risk specialties (OR, 0.55; 95% CI, 0.27-1.12;P= .10) and those in low- risk specialties (0.51; 0.26-1.00;P= .05). COMMENT In this cross-sectional study, we found that physicians who used EHRs were less likely to have paid malprac- tice claims compared with physicians who did not use EHRs. Although this relationship is partially con- founded by physician sex, year of medical school gradu- ation, and practice size, the presence of EHR appears to be associated with a lower malpractice risk. This impres- sion is further strengthened by the observed trend among physicians with EHRs that suggests lower rates of paid malpractice claims among more avid users of their EHR systems.

Few previous studies have directly examined the re- lationship between EHR adoption and malpractice claims.Although 1 study 18found that computerized physician order entry was associated with a lower rate of malprac- tice claims in the hospital, studies of HIT and malprac- tice claims in the ambulatory setting have been lacking.

The results of this study support the hypothesis that EHR adoption and use lead to improved quality of care and patient safety, resulting in fewer adverse events and fewer paid malpractice claims. A number of mechanisms could be responsible for a lower frequency of malpractice claims.

For example, use of EHRs may lead to fewer diagnostic errors, improved follow-up of abnormal test results, bet- ter guideline adherence, and fewer adverse clinical events.

Alternatively, EHRs may be facilitating more extensive Table 2. Characteristics of Physicians With Malpractice Settlements a CharacteristicPhysicians With Malpractice Settlements (n = 105)Physicians Without Malpractice Settlements (n = 1035)PValue Age, mean (SD), y 49.5 (10.7) 54.1 (8.7) .001 Median year of medical school graduation (IQR)1977 (1971-1985) 1986 (1977-1992) .001 Graduated medical school before 198062 (59.0) 346 (33.4) .001 Women 20 (19.0) 337 (33.1) .003 White race 88 (86.3) 839 (84.7) .77 Practice size .001 Solo practice 45 (43.7) 276 (27.1) 2-4 Physicians 30 (29.1) 309 (30.4) 5-9 Physicians 20 (19.4) 221 (21.7) 10 Physicians 8 (7.8) 211 (20.8) Primary care b 39 (37.5) 407 (39.9) .67 EHR adoption 23 (21.9) 356 (34.4) .009 Abbreviations: EHR, electronic health record; IQR, interquartile range. aData are presented as number (percentage) of study participants unless otherwise indicated. Data were missing for sex (n = 18), race (n = 48), practice size (n = 20), specialty (n = 17), and any component of EHRs in practice (n = 1).

Denominators vary because of missing data.

bPrimary care included family practice, general internal medicine, general pediatrics, combined medicine and pediatrics, and geriatrics.

Table 3. Correlates of Paid Malpractice Claims From a Logistic Regression Model a CharacteristicAdjusted OR (95% CI)PValue EHR adoption 0.69 (0.40-1.20) .18 Medical school graduation year0.96 (0.95-0.98) .001 Women 0.59 (0.34-1.02) .06 White race 0.92 (0.49-1.71) .78 Practice size Solo practice 2.39 (1.03-5.53) .04 2-4 Physicians 2.20 (0.95-5.10) .07 5-9 Physicians 2.30 (0.97-5.47) .06 10 Physicians 1 [Reference] Primary care 1.00 (0.64-1.56) .99 Abbreviations: CI, confidence interval; EHR, electronic health record; OR, odds ratio.

aModel adjusted for EHR adoption status, year of medical school graduation, sex, race, practice size, and specialty. (REPRINTED) ARCH INTERN MED/ VOL 168 (NO. 21), NOV 24, 2008 WWW. ARCHINTERNMED.COM 2365 ©2008 American Medical Association. All rights reserved.Downloaded From: https://jamanetwork.com/ on 05/30/2020 and more legible documentation of medical practice, re- sulting in stronger legal defenses when malpractice suits are filed. In addition, EHRs may be enhancing patient- physician communication, an important determinant of malpractice claims. 22 If confirmed in future studies, the observed relation- ship between EHR adoption and paid malpractice claims could have implications for physicians and malpractice insurers. First, for practices struggling to reconcile the expense of investing in HIT, 19the potential benefit of fewer malpractice claims may tip the scale toward EHR adop- tion. Second, if EHRs are proved to be an effective tool in minimizing tort claims and improving patient safety, insurance companies may lower malpractice premiums for practices with EHRs. Currently, most liability insur- ers adjust physicians’ premiums by specialty, location, and past malpractice experience. 23,24 We are familiar with 1 carrier that has instituted a premium credit for physi- cians and practices with EHRs. 25If other carriers follow, lower malpractice premiums could provide an addi- tional incentive for clinicians considering the purchase of an EHR system for an office practice.

The relationship between EHR adopters and malprac- tice claims also has potential health care policy implica- tions. If confirmed in future studies, our results may give the federal government and other payers further incen- tive to fund subsidies for EHR adoption because of the additional reduction in health care costs through a de- crease in medical liability and associated costs.

A strength of this study is its use of verified paid mal- practice claims rather than claims filed. Because most closed malpractice claims have proved negligence, 14by identifying only claims that had been paid out rather than those filed, we were able to exclude lawsuits whose out- come was still in doubt, as well as so-called frivolous law- suits. In addition, our survey enabled us to examine not only EHR adoption but also use of key EHR functions as they relate to paid malpractice claims.

This study has several important limitations. Al- though provocative, our findings are inconclusive. They should not be interpreted as establishing a causal link be- tween EHR adoption and the prevention of malpractice claims. It is possible that unmeasured confounding ac- counts for the fact that physicians who use EHRs may be less likely to be subjects of successful malpractice liti- gation. For instance, use of EHR may be an intermedi- ate marker for preestablished physician behaviors or prac- tice variations that may lead to a reduction in malpractice claims.

Another limitation is our data source for malpractice claims, the BRM Web site, which indicates only paid mal- practice settlements; malpractice suits that were dis- missed or still in process are not included. Further- more, detailed information regarding the nature of the claim is not available. Relying on paid malpractice settle- ments created a 5-year or longer time lag between the time when the putative error and adverse event oc- curred and the time when the claim was settled and paid.

Moreover, because the BRM posts data on physicians only for the preceding 10 years, additional malpractice claims for physicians in practice earlier than this period may not have been captured.To compensate for these cross-sectional limitations, future studies would ideally include a longitudinal data source that would record the physician’s date of EHR implementation and use, along with the date of the li- able incident, filing date, and its outcome. Such studies would require an observation period of many years to ac- count for the time lag between the malpractice-related event and the consequent settlement process. We con- ducted an exploratory analysis to isolate the temporal re- lationship between EHR adoption and paid malpractice settlements that yielded results consistent with the pri- mary analyses; however, this exploratory analysis must be interpreted with caution because of the small num- ber of outcomes observed and the resulting imprecision of the effect estimate.

An additional limitation is that this study was con- ducted among physicians licensed in Massachusetts, and the results may not be applicable to the remainder of the nation. On the basis of a previous analysis, 19Massachu- setts EHR adoption rates (23% of practices and 45% of physicians) are considerably higher than rates observed nationwide. The percentage of Massachusetts physi- cians with malpractice claims may also be different from the national average. The Kaiser Family Foundation re- ported that, in 2007, Massachusetts had 8 claims per 1000 nonfederal physicians, half of the national average. 26No- tably, this rate is consistent with a 2004 BRM report27that reviewed malpractice data from 1994 to 2003. Whether the relationship between EHRs and malpractice claims differs across states remains to be studied.

In conclusion, the results of this study should be con- sidered preliminary. The findings suggest that physi- cians with EHRs may have a lower prevalence of paid mal- practice claims than physicians without EHRs. Further study is needed to clarify this relationship and the mecha- nisms that may underlie it.

Accepted for Publication:June 2, 2008.

Correspondence:Steven R. Simon, MD, MPH, Depart- ment of Ambulatory Care and Prevention, Harvard Medi- cal School and Harvard Pilgrim Health Care, 133 Brook- line Ave, Sixth Floor, Boston, MA 02215 (steven_simon @hphc.org).

Author Contributions:Drs Virapongse and Simon had full access to all of the data in the study and take respon- sibility for the integrity of the data and the accuracy of the data analysis.Study concept and design:Virapongse and Simon.Acquisition of data:Virapongse, Bates, Jenter, Volk, and Simon.Analysis and interpretation of data:Vi- rapongse, Simon, Volk, and Shi.Drafting of the manu- script:Virapongse and Simon.Critical revision of the manu- script for important intellectual content: Virapongse, Bates, Shi, Jenter, Volk, Kleinman, Sato, and Simon.Statistical analysis: Virapongse, Shi, and Kleinman.Administra- tive, technical, or material support:Jenter and Volk.

Financial Disclosure:Dr Virapongse reports that dur- ing the writing of the manuscript she was a fellow at Blue Cross, Blue Shield of Massachusetts.

Funding/Support:This study was funded in part by the Agency for Healthcare Research and Quality coopera- tive agreement 1UC1HS015397-01 and the Massachu- setts e-Health Collaborative. (REPRINTED) ARCH INTERN MED/ VOL 168 (NO. 21), NOV 24, 2008 WWW. ARCHINTERNMED.COM 2366 ©2008 American Medical Association. All rights reserved.Downloaded From: https://jamanetwork.com/ on 05/30/2020 Role of the Sponsors:The Agency for Healthcare Re- search and Quality and the Massachusetts e-Health Col- laborative had no role in the design and conduct of the study; collection, management, analysis, and interpreta- tion of the data; and preparation, review, or approval of the manuscript.

Disclaimer:The content is solely the responsibility of the authors and does not necessarily represent the official views of the Agency for Healthcare Research and Quality, or of the Massachusetts e-Health Collaborative.

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