Regenstrief Institute

About: Regenstrief Institute is a nonprofit organization based out in Indianapolis, Indiana, United States. It is known for research contribution in the topics: Health care & Population. The organization has 742 authors who have published 2042 publications receiving 96966 citations.

Patient Preferences in Controlling Access to Their Electronic Health Records: a Prospective Cohort Study in Primary Care

Abstract: Introduction Previous studies have measured individuals’ willingness to share personal information stored in electronic health records (EHRs) with health care providers, but none has measured preferences among patients when they are allowed to determine the parameters of provider access.

Number needed to treat in cardiac rehabilitation.

Abstract: Clinicians, patients, and health policy-makers must judge whether healthcare interventions are worth the side effects, inconvenience, and costs. The number needed to treat (NNT) provides an estimate of the number of patients who need to be treated to attain an additional favorable outcome, or to prevent an additional adverse outcome, and is the reciprocal of the absolute risk reduction. The closer the NNT is to 1.0-meaning that every patient who is treated achieves a benefit-the more effective the treatment. Traditionally, mortality has been considered a primary outcome measure of the effectiveness of cardiac rehabilitation and, if the event rates in two groups (ie, rehabilitation and usual care) are known, the absolute risk reduction can be calculated and the NNT estimated. Mortality data were derived from three meta-analyses of cardiac rehabilitation trials: one published in 1988 (n = 3614), one in 1989 (n = 4247), and one in 2001 (n = 7683). The respective estimated NNT for mortality in the meta-analyses were 32, 46, and 72 (95% confidence intervals [95% CI] 19, 1403). Improved exercise tolerance and patient-perceived health-related quality of life (HRQL) are also considered important and attainable outcomes of cardiac rehabilitation but are continuous, not dichotomous, variables. If the minimal important difference for a continuous outcome is known, then the proportions of patients who improve, remain the same, or deteriorate can be determined and the NNT estimated. Exercise tolerance and HRQL data from two randomized controlled trials of 8 weeks of rehabilitation after myocardial infarction, the Cardiac Rehabilitation in Advanced Age trial (CR-AGE; n = 270) and the McMaster Early Rehabilitation Study (MERS; n = 201) were used to estimate the NNT. In CR-AGE, the improvement in exercise tolerance was significantly greater in the rehabilitation than usual care group and the estimated NNT was 5 (95% CI 3, 13). The generic global HRQL score increased significantly in CR-AGE with rehabilitation with an estimated NNT of 12 (95% CI 5, 26) but, as the subscale group differences were not significant, the NNT was not estimated. The NNT for exercise tolerance was not estimated in MERS, as the group difference was not significant. On the other hand, specific HRQL scores in MERS increased significantly with rehabilitation giving an estimated NNT for global HRQL of 6 (95% CI 3, 21) and 6 to 10 for the HRQL sub-scales. The data and the estimated NNT from the meta-analyses of cardiac rehabilitation in large numbers of patients suggest a limited mortality effect, probably reflecting current cardiology practice. The estimated NNT from the two trials with relatively small numbers of patients suggest inconsistent exercise tolerance effects and a relatively short duration for improved HRQL. Along with the classic reporting scales, information about clinical and laboratory variables, and patient preferences, the NNT is a useful additional measure of effectiveness that provides both clinicians and patients with information about the impact of cardiac rehabilitation as secondary prevention therapy.

Impact of Race and Sex on Pain Management by Medical Trainees: A Mixed Methods Pilot Study of Decision Making and Awareness of Influence

Abstract: Objective Previous research suggests female and black patients receive less optimal treatment for their chronic pain compared with male and white patients. Provider-related factors are hypothesized to contribute to unequal treatment, but these factors have not been examined extensively. This mixed methods investigation examined the influence of patients' demographic characteristics on providers' treatment decisions and providers' awareness of these influences on their treatment decisions. Methods Twenty medical trainees made treatment decisions (opioid, antidepressant, physical therapy) for 16 virtual patients with chronic low back pain; patient sex and race were manipulated across patients. Participants then indicated from a provided list the factors that influenced their treatment decisions, including patient demographics. Finally, individual interviews were conducted to discuss the role of patient demographics on providers' clinical decisions. Results Individual regression analyses indicated that 30% of participants were reliably influenced by patient sex and 15% by patient race when making their decisions (P < 0.05 or P < 0.10). Group analyses indicated that white patients received higher antidepressant recommendations, on average, than black patients (P < 0.05). Half of the medical trainees demonstrated awareness of the influence of demographic characteristics on their decision making. Participants, regardless of whether they were influenced by patients' demographics, discussed themes related to patient sex and race; however, participants' discussion of patient demographics in the interviews did not always align with their online study results. Conclusions These findings suggest there is a considerable variability in the extent to which medical trainees are influenced by patient demographics and their awareness of these decision making influences.

Using natural language processing to improve accuracy of automated notifiable disease reporting.

Abstract: We examined whether using a natural language processing (NLP) system results in improved accuracy and completeness of automated electronic laboratory reporting (ELR) of notifiable conditions. We used data from a community-wide health information exchange that has automated ELR functionality. We focused on methicillin-resistant Staphylococcus Aureus (MRSA), a reportable infection found in unstructured, free-text culture result reports. We used the Regenstrief EXtraction tool (REX) for this work. REX processed 64,554 reports that mentioned MRSA and we compared its output to a gold standard (human review). REX correctly identified 39,491(99.96%) of the 39,508 reports positive for MRSA, and committed only 74 false positive errors. It achieved high sensitivity, specificity, positive predicted value and F-measure. REX identified over two times as many MRSA positive reports as the ELR system without NLP. Using NLP can improve the completeness and accuracy of automated ELR.