BackgroundPoor 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.MethodsThe 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.ResultsFollowing 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.ConclusionWe 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.
BackgroundOf 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.MethodsOne 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.ResultsUsing 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).ConclusionsBy 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.
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.
BackgroundFrom 2014 to 2017, more than 1000 diagnostic companies were launched, securing more than US$10 billion in investment.MethodsWe performed an in-depth exploration of 28 diagnostic companies to differentiate successful and failed startups, plus a third ‘Zombie’ state where companies have achieved financial solvency but without long-term viability.ResultsFrom these data, we created a five-phase, 13-item framework indicating the corporate health of a diagnostic company as it progresses from conception to commercialisation. We found 6 successful companies, 14 failures and 8 Zombies. On a scale of 0–26 points (two points per item), successful companies averaged 24.5 points (range 22–26), failures averaged 4.5 (range 0–16) and Zombies averaged 12.3 (range 3–23) (p<0.001). To determine if there was any predictivity to this framework, we looked at only the first two phases (concept and feasibility/planning) of progress and found a distinct gradient in success potential based solely on these first two phases.ConclusionOur five-phase framework generated a score that could predict diagnostic companies more likely to successfully and sustainably enter the market from those more likely to fail.
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