Randomized Trial to Improve Primary Care Patient Management and Patient Outcomes Using a Drug-Drug Interaction Test: Confirmation of the DECART Simulated Patient Clinical Utility Trial Results.

Drug-drug interactions (DDIs) are a serious problem in the healthcare system, leading to excess healthcare utilization and costs. We conducted a second prospective randomized, controlled trial to further establish the real-world clinical utility of a novel assay that objectively identifies potentially serious DDIs in real-world patients. Re-recruiting primary care physicians (PCPs) from our first randomized, controlled, simulated-patients study on DDIs, we experimentally introduced a definitive, urine-based mass spectrometry test intervention that the physicians could use when caring for their eligible patients. Patients were eligible if taking four or more prescription medications or suspected of taking other non-prescribed substances with potential medication interactions. The primary outcome was whether DDI testing changed clinical care. We explored a secondary outcome to see if the change in practice improved symptoms in patients with potential DDIs. A total of 169 control and 162 intervention patients were enrolled in the study, and their medical records were abstracted. In real-world patients, intervention physicians identified and/or treated a DDI at 3.0x the rate in their patient population compared to controls (21.6% vs. 7.1%, p < 0.001). Intervention physicians were more likely to discontinue or adjust the interacting agent compared to controls (62.9% vs. 8.3%, p = 0.001), and patient-reported symptoms also significantly declined (29.6% vs. 20.1%, p = 0.045). These results were nearly identical to concurrent measurements that used simulated patients, wherein intervention was more likely to both make a DDI diagnosis (56.3% vs. 21.6%, p < 0.001) and stop the interacting medications (58.3% versus 26.6%, p < 0.001). Bringing a new diagnostic test to market, particularly for an under-recognized clinical problem, requires robust data on both clinical validity and clinical utility. The results of this follow-up study showed that the use of DDI testing in real-world patients significantly improved (1) primary care patient management of drug interactions and (2) patient outcomes.

A Direct Comparison of the Clinical Practice Patterns of Advanced Practice Providers and Doctors.

BACKGROUND: Rising health care costs, physician shortages, and an aging patient population have increased the demand and utilization of advanced practice providers (APPs). Despite their expanding role in care delivery, little research has evaluated the care delivered by APPs compared with physicians. METHODS: We used clinical patient simulations to measure and compare the clinical care offered by APPs and physicians, collecting data from 4 distinct health care systems/hospitals in the United States between 2013 and 2017. Specialties ranged from primary care to hospital medicine and oncology. Primary study outcomes were to 1) measure any differences in practice patterns between APPs and physicians, and 2) determine whether the use of serial measurement and feedback could mitigate any such differences. RESULTS: At baseline, we found no major differences in overall performance of APPs compared with physicians (P = .337). APPs performed 3.2% better in history taking (P = .013) and made 10.5% fewer unnecessary referrals (P = .025), whereas physicians ordered 17.6% fewer low-value tests per case (P = .042). Regardless of specialty or site, after 4 rounds of serial measurement and provider-specific feedback, APPs and physicians had similar increases in average overall scores-7.4% and 7.6%, respectively (P < .001 for both). Not only did both groups improve, but practice differences between the groups disappeared, leading to a 9.1% decrease in overall practice variation. CONCLUSIONS: We found only modest differences in quality of care provided by APPs and physicians. Importantly, both groups improved their performance with serial measurement and feedback so that after 4 rounds, the original differences were mitigated entirely and overall variation significantly reduced. Our data suggest that APPs can provide high quality care in multiple clinical settings.

Impact of a protein-based assay that predicts prostate cancer aggressiveness on urologists' recommendations for active treatment or active surveillance: a randomized clinical utility trial.

BACKGROUND: Of the more than 1.1 million men diagnosed worldwide annually with prostate cancer, the majority have indolent tumors. Distinguishing between aggressive and indolent cancer is an important clinical challenge. The current approaches for assessing tumor aggressiveness are recognized as insufficient. A validated protein-based assay has been shown to predict tumor aggressiveness from prostate biopsy. The main objective of this study was to measure the clinical utility of this new assay in the management of early-stage prostate cancer. METHODS: One hundred twenty nine board-certified urologists were asked to participate in a randomized, two-arm experiment. We collected data over 2 rounds using simulated clinical cases administered via an online platform. The cases were all newly diagnosed Gleason 3 + 3 or 3 + 4 prostate camcer patients. Urologists in the intervention arm received a 15-min webinar on this protein-based assay and given assay test results for their simulated patients in round 2. Each case had a preferred recommendation of either active surveillance or active treatment. The measured outcome was rate of preferred recommendation, defined as urologists who recommended the proper treatment course. Analyses were done using difference-in-difference estimations. RESULTS: Using multinomial logistical regression, urologists who were given the assay results were significantly more likely to choose the preferred recommendation (active surveillance or active treatment) compared to controls (p = 0.004). These urologists were also significantly more likely to involve their patients in the treatment decision compared to controls (p = 0.001). CONCLUSIONS: By providing additional information to inform the physician's treatment plan, a protein-based assay shows demonstrable clinical utility confirmed through a rigorous randomized controlled study design and regression analyses to test for effects.

Improving Clinical Practice Using a Novel Engagement Approach: Measurement, Benchmarking and Feedback, A Longitudinal Study.

BACKGROUND: Poor clinical outcomes are caused by multiple factors such as disease progression, patient behavior, and structural elements of care. One other important factor that affects outcome is the quality of care delivered by a provider at the bedside. Guidelines and pathways have been developed with the promise of advancing evidence-based practice. Yet, these alone have shown mixed results or fallen short in increasing adherence to quality of care. Thus, effective, novel tools are required for sustainable practice change and raising the quality of care. METHODS: The study focused on benchmarking and measuring variation and improving care quality for common types of breast cancer at four sites across the United States, using a set of 12 Clinical Performance and Value(®) (CPV(®)) vignettes per site. The vignettes simulated online cases that replicate a typical visit by a patient as the tool to engage breast cancer providers and to identify and assess variation in adherence to evidence-based practice guidelines and pathways. RESULTS: Following multiple rounds of CPV measurement, benchmarking and feedback, we found that scores had increased significantly between the baseline round and the final round (P < 0.001) overall and for all domains. By round 4 of the study, the overall score increased by 14% (P < 0.001), and the diagnosis with treatment plan domain had an increase of 12% (P < 0.001) versus baseline. CONCLUSION: We found that serially engaging breast cancer providers with a validated clinical practice engagement and measurement tool, the CPVs, markedly increased quality scores and adherence to clinical guidelines in the simulated patients. CPVs were able to measure differences in clinical skill improvement and detect how fast improvements were made.