Lisa M. Kern

Electronic Prescribing Improves Medication Safety in Community-Based Office Practices

BACKGROUNDAlthough electronic prescribing (e-prescribing) holds promise for preventing prescription errors in the ambulatory setting, research on its effectiveness is inconclusive.OBJECTIVETo assess the impact of a stand-alone e-prescribing system on the rates and types of ambulatory prescribing errors.DESIGN, PARTICIPANTSProspective, non-randomized study using pre-post design of 15 providers who adopted e-prescribing with concurrent controls of 15 paper-based providers from September 2005 through June 2007.INTERVENTIONUse of a commercial, stand-alone e-prescribing system with clinical decision support including dosing recommendations and checks for drug-allergy interactions, drug-drug interactions, and duplicate therapies.MAIN MEASURESPrescribing errors were identified by a standardized prescription and chart review.KEY RESULTSWe analyzed 3684 paper-based prescriptions at baseline and 3848 paper-based and electronic prescriptions at one year of follow-up. For e-prescribing adopters, error rates decreased nearly sevenfold, from 42.5 per 100 prescriptions (95% confidence interval (CI), 36.7–49.3) at baseline to 6.6 per 100 prescriptions (95% CI, 5.1–8.3) one year after adoption (p < 0.001). For non-adopters, error rates remained high at 37.3 per 100 prescriptions (95% CI, 27.6–50.2) at baseline and 38.4 per 100 prescriptions (95% CI, 27.4–53.9) at one year (p = 0.54). At one year, the error rate for e-prescribing adopters was significantly lower than for non-adopters (p < 0.001). Illegibility errors were very high at baseline and were completely eliminated by e-prescribing (87.6 per 100 prescriptions at baseline for e-prescribing adopters, 0 at one year).CONCLUSIONSPrescribing errors may occur much more frequently in community-based practices than previously reported. Our preliminary findings suggest that stand-alone e-prescribing with clinical decision support may significantly improve ambulatory medication safety.TRIAL REGISTRATIONClinicalTrials.gov, Taconic Health Information Network and Community (THINC), NCT00225563, http://clinicaltrials.gov/ct2/show/NCT00225563?term=Kaushal&rank=6.

A Simple Algorithm to Predict Incident Kidney Disease

BACKGROUND Despite the growing burden of chronic kidney disease (CKD), there are no algorithms (to our knowledge) to quantify the effect of concurrent risk factors on the development of incident disease. METHODS A combined cohort (N = 14 155) of 2 community-based studies, the Atherosclerosis Risk in Communities Study and the Cardiovascular Health Study, was formed among men and women 45 years or older with an estimated glomerular filtration rate (GFR) exceeding 60 mL/min/1.73 m(2) at baseline. The primary outcome was the development of a GFR less than 60 mL/min/1.73 m(2) during a follow-up period of up to 9 years. Three prediction algorithms derived from the development data set were evaluated in the validation data set. RESULTS The 3 prediction algorithms were continuous and categorical best-fitting models with 10 predictors and a simplified categorical model with 8 predictors. All showed discrimination with area under the receiver operating characteristic curve in a range of 0.69 to 0.70. In the simplified model, age, anemia, female sex, hypertension, diabetes mellitus, peripheral vascular disease, and history of congestive heart failure or cardiovascular disease were associated with the development of a GFR less than 60 mL/min/1.73 m(2). A numeric score of at least 3 using the simplified algorithm captured approximately 70% of incident cases (sensitivity) and accurately predicted a 17% risk of developing CKD (positive predictive value). CONCLUSIONS An algorithm containing commonly understood variables helps to stratify middle-aged and older individuals at high risk for future CKD. The model can be used to guide population-level prevention efforts and to initiate discussions between practitioners and patients about risk for kidney disease.

HEAL NY: Promoting Interoperable Health Information Technology In New York State

Through a novel, ambitious program called HEAL NY, New York State plans to invest

Accuracy of Electronically Reported “Meaningful Use” Clinical Quality Measures: A Cross-sectional Study

250 million in health information technology (IT) that can be linked electronically to other health IT systems. In contrast to high rates of closure by other organizations attempting health information exchange (HIE), 100 percent of HEAL NY Phase 1 grantees still existed two years after awards were announced, 85 percent were still pursuing HIE, and 35 percent had actual users. The number of grantees meeting basic criteria for regional health information organizations (RHIOs) increased. Although it is early, lessons learned can inform state-based initiatives nationwide.

The Triangle Model for evaluating the effect of health information technology on healthcare quality and safety

BACKGROUND The federal Electronic Health Record Incentive Program requires electronic reporting of quality from electronic health records, beginning in 2014. Whether electronic reports of quality are accurate is unclear. OBJECTIVE To measure the accuracy of electronic reporting compared with manual review. DESIGN Cross-sectional study. SETTING A federally qualified health center with a commercially available electronic health record. PATIENTS All adult patients eligible in 2008 for 12 quality measures (using 8 unique denominators) were identified electronically. One hundred fifty patients were randomly sampled per denominator, yielding 1154 unique patients. MEASUREMENTS Receipt of recommended care, assessed by both electronic reporting and manual review. Sensitivity, specificity, positive and negative predictive values, positive and negative likelihood ratios, and absolute rates of recommended care were measured. RESULTS Sensitivity of electronic reporting ranged from 46% to 98% per measure. Specificity ranged from 62% to 97%, positive predictive value from 57% to 97%, and negative predictive value from 32% to 99%. Positive likelihood ratios ranged from 2.34 to 24.25 and negative likelihood ratios from 0.02 to 0.61. Differences between electronic reporting and manual review were statistically significant for 3 measures: Electronic reporting underestimated the absolute rate of recommended care for 2 measures (appropriate asthma medication [38% vs. 77%; P < 0.001] and pneumococcal vaccination [27% vs. 48%; P < 0.001]) and overestimated care for 1 measure (cholesterol control in patients with diabetes [57% vs. 37%; P = 0.001]). LIMITATION This study addresses the accuracy of the measure numerator only. CONCLUSION Wide measure-by-measure variation in accuracy threatens the validity of electronic reporting. If variation is not addressed, financial incentives intended to reward high quality may not be given to the highest-quality providers. PRIMARY FUNDING SOURCE Agency for Healthcare Research and Quality.

Healthcare Consumers’ Attitudes Towards Physician and Personal Use of Health Information Exchange

With the proliferation of relatively mature health information technology (IT) systems with large numbers of users, it becomes increasingly important to evaluate the effect of these systems on the quality and safety of healthcare. Previous research on the effectiveness of health IT has had mixed results, which may be in part attributable to the evaluation frameworks used. The authors propose a model for evaluation, the Triangle Model, developed for designing studies of quality and safety outcomes of health IT. This model identifies structure-level predictors, including characteristics of: (1) the technology itself; (2) the provider using the technology; (3) the organizational setting; and (4) the patient population. In addition, the model outlines process predictors, including (1) usage of the technology, (2) organizational support for and customization of the technology, and (3) organizational policies and procedures about quality and safety. The Triangle Model specifies the variables to be measured, but is flexible enough to accommodate both qualitative and quantitative approaches to capturing them. The authors illustrate this model, which integrates perspectives from both health services research and biomedical informatics, with examples from evaluations of electronic prescribing, but it is also applicable to a variety of types of health IT systems.

Measuring the Effects of Health Information Technology on Quality of Care: A Novel Set of Proposed Metrics for Electronic Quality Reporting

Health information exchange (HIE), the electronic transmission of patient medical information across healthcare institutions, is on the forefront of the national agenda for healthcare reform. As healthcare consumers are critical participants in HIE, understanding their attitudes toward HIE is essential. To determine healthcare consumers’ attitudes toward physician and personal use of HIE, and factors associated with their attitudes. Cross-sectional telephone survey. English-speaking residents of the Hudson Valley of New York. Consumer reported attitudes towards HIE. Of 199 eligible residents contacted, 170 (85%) completed the survey: 67% supported physician HIE use and 58% reported interest in using HIE themselves. Multivariate analysis suggested supporters of physician HIE were more likely to be caregivers for chronically ill individuals (OR 4.6, 95% CI 1.06, 19.6), earn more than

Association between use of a health information exchange system and hospital admissions

100,000 yearly (OR 3.5, 95% CI 1.2, 10.0), and believe physician HIE would improve the privacy and security of their medical records (OR 2.9, 95% CI 1.05, 7.9). Respondents interested in using personal HIE were less likely to be female (OR 0.4, 95% CI 0.1, 0.98), and more likely to be frequent Internet-users (OR 3.3, 95% CI 1.03, 10.6), feel communication among their physicians was inadequate (OR 6.7, 95% CI 1.7, 25.3), and believe personal HIE use would improve communication with their physicians (OR 4.7, 95% CI 1.7, 12.8). Consumer outreach to gain further support for ongoing personal and physician HIE efforts is needed and should address consumer security concerns and potential disparities in HIE acceptance and use.BACKGROUNDHealth information exchange (HIE), the electronic transmission of patient medical information across healthcare institutions, is on the forefront of the national agenda for healthcare reform. As healthcare consumers are critical participants in HIE, understanding their attitudes toward HIE is essential.OBJECTIVETo determine healthcare consumers’ attitudes toward physician and personal use of HIE, and factors associated with their attitudes.DESIGNCross-sectional telephone survey.PARTICIPANTSEnglish-speaking residents of the Hudson Valley of New York.MAIN MEASUREConsumer reported attitudes towards HIE.KEY RESULTSOf 199 eligible residents contacted, 170 (85%) completed the survey: 67% supported physician HIE use and 58% reported interest in using HIE themselves. Multivariate analysis suggested supporters of physician HIE were more likely to be caregivers for chronically ill individuals (OR 4.6, 95% CI 1.06, 19.6), earn more than

Health Care Consumers’ Preferences Around Health Information Exchange

100,000 yearly (OR 3.5, 95% CI 1.2, 10.0), and believe physician HIE would improve the privacy and security of their medical records (OR 2.9, 95% CI 1.05, 7.9). Respondents interested in using personal HIE were less likely to be female (OR 0.4, 95% CI 0.1, 0.98), and more likely to be frequent Internet-users (OR 3.3, 95% CI 1.03, 10.6), feel communication among their physicians was inadequate (OR 6.7, 95% CI 1.7, 25.3), and believe personal HIE use would improve communication with their physicians (OR 4.7, 95% CI 1.7, 12.8).CONCLUSIONSConsumer outreach to gain further support for ongoing personal and physician HIE efforts is needed and should address consumer security concerns and potential disparities in HIE acceptance and use.

The potential for community-based health information exchange systems to reduce hospital readmissions

BACKGROUND Electronic health records (EHRs), in combination with health information exchange, are being promoted in the United States as a strategy for improving quality of care. No single metric set exists for measuring the effectiveness of these interventions. A set of quality metrics was sought that could be retrieved electronically and would be sensitive to the changes in quality that EHRs with health information exchange may contribute to ambulatory care. METHODS A literature search identified quality metric sets for ambulatory care. Two rounds of quantitative rating of individual metrics were conducted. Metrics were developed de novo to capture additional expected effects of EHRs with health information exchange. A 36-member national expert panel validated the rating process and final metric set. RESULTS Seventeen metric sets containing 1,064 individual metrics were identified; 510 metrics met inclusion criteria. Two rounds of rating narrowed these to 59 metrics and then to 18. The final 18 consisted of metrics for asthma, cardiovascular disease, congestive heart failure, diabetes, medication and allergy documentation, mental health, osteoporosis, and prevention. Fourteen metrics were developed de novo to address test ordering, medication management, referrals, follow-up after discharge, and revisits. DISCUSSION The novel set of 32 metrics is proposed as suitable for electronic reporting to capture the potential quality effects of EHRs with health information exchange. This metric set may have broad utility as health information technology becomes increasingly common with funding from the federal stimulus package and other sources. This work may also stimulate discussion on improving how data are entered and extracted from clinically rich, electronic sources, with the goal of more accurately measuring and improving care.