Atheoretical Versus Theory-Based Approaches in Promoting Safer ADHD-Medication Prescribing for Adults

Gaps between treatment guidelines and medical decisions persist despite interventions with physicians, which are mostly atheoretical. The purpose of this retrospective cross-sectional study was to compare atheoretical and theory-based logistic regression models of a binary outcome: potentially unsafe prescribing of attention-deficit hyperactivity disorder (ADHD) medications to adults. Social cognitive theory and self-determination theory provided the framework for the study. Predictors were framed as social cognitive theoretical constructs: knowledge (e.g., physician specialty) and environmental influence (e.g., interventions). Atheoretical hypotheses were based on legislation mandating meaningful use of electronic health records and computerized decision support (CDS). Theory-based hypotheses were derived from literature on cognition in medicine and on the controlled motivation construct in self-determination theory. Research questions addressed associations of CDS and meaningful use with the outcome and fit of competing models. The sample included office-based physician visits made by patients aged \u3e 17 years with ADHD (n = 810) or potentially unsafe medical conditions (n = 9,101), recorded in a U.S. database in 2014–2016. Findings for the atheoretical model were reduced odds of the outcome with CDS, and nonsignificant improvement in model fit using theory. Supporting the self-determination theory-based hypothesis, odds were increased with meaningful use. This study adds to research suggesting autonomy as a core issue in medicine. Positive social change may result from psychology-based strategies to empower physicians through participation in developing clinically relevant information systems

Clinical Quality Indicators and Provider Financial Incentives: Does Money Matter?

"Simply put, if reimbursement can drive utilization and utilization can drive outcome, reimbursement can drive outcome." 1 I n the decades-old debate over how best to finance and deliver health care in the United States, a nearly ubiquitous complaint about most systems of physician and hospital reimbursement is that payments are made based on the services delivered regardless of the quality of care delivered, providing no incentive-some say a disincentive-for quality improvement. 2,3 Advocates of "pay-for-performance" (P4P) systems of health care reimbursement argue that the concept of paying more to those who produce better outcomes, a "bedrock principle" in efforts to "reduce error and reinforce best practices" in other industries, should become "a top national priority" in "the campaign to rally our underperforming health care system." In proposals to improve health care systems, high-level enthusiasm is not necessarily an indicator of high-quality evidence, and P4P is no exception. Editorialists George Diamond and Sanjay Kaul, both cardiologists and keen observers of quality of evidence in health care decision making, wrote in 2009 that rapid proliferation of P4P systems "is occurring despite a paucity of empirical evidence that [they] actually deliver on their promise to improve the quality and reduce the cost of health care. There are essentially no randomized controlled trials (RCTs) demonstrating the effectiveness of [P4P] programs and very few reports in the literature that analyze the existing programs." 1 The point made by Diamond and Kaul is welltaken. As we have observed previously, the health care research literature is replete with examples of schemes that were widely (sometimes wildly) supported based on weak observational evidence but refuted and ultimately abandoned after being tested with more rigorous research designs. Effects of Quality Improvement Interventions Alone and with Financial Incentives In this issue of JMCP, Brackbill et al. report the results of a quality improvement project undertaken to increase the percentage of patients receiving discharge orders for chronic aspirin therapy following a hospitalization for acute myocardial infarction (AMI) or coronary artery bypass graft (CABG). 5 Using a pre-intervention versus post-intervention study design, Brackbill et al. found that an intervention consisting of provider education coupled with the placement of a colorful "prescription" for aspirin in the inpatient chart of patients clinically eligible for aspirin therapy was associated with a change in the aspirin discharge order rate from 94.9% pre-intervention to 98.9% post-intervention (P = 0.012). When analyzed by subgroup, the relationship between the intervention and aspirin discharge order rate was statistically significant for patients hospitalized for CABG (change from 91.5% to 100.0%, P = 0.016) but not for AMI (change from 96.6% to 98.5%, P = 0.263). The efforts of Brackbill et al. in using a novel approach to improve an important quality metric are commendable. Nonetheless, their results illustrate the challenges experienced by providers and payers that try to "move the needle" of compliance with treatment guidelines, especially when baseline compliance is high. Brackbill et al. report that quality assurance audits conducted shortly after the start of the project revealed substantial implementation problems; the aspirin "prescription" had been placed in only 25% of the charts of clinically eligible patients. Education of providers that was initially conducted pre-intervention had to be repeated twice in the project's post-implementation phase

Use of power-law analysis to predict abuse or diversion of prescribed medications: proof-of-concept mathematical exploration

Abstract Objective To conduct a proof-of-concept study comparing Lorenz-curve analysis (LCA) with power-law (exponential function) analysis (PLA), by applying segmented regression modeling to 1-year prescription claims data for three medications—alprazolam, opioids, and gabapentin—to predict abuse and/or diversion using power-law zone (PLZ) classification. Results In 1-year baseline observation, patients classified into the top PLZ groups (PLGs) were demographically and diagnostically similar to those in Lorenz-1 (top 1% of utilizers) and Lorenz-25 (top 25%). For prediction of follow-up (6-month post-baseline) Lorenz-1 use of alprazolam and opioids (i.e., potential abuse/diversion), PLA had somewhat lower sensitivity compared with LCA (83.5–95.4% vs. 99.5–99.9%, respectively) but better specificity (98.2–98.8% vs. 75.5%) and much better positive predictive value (PPV; 34.5–45.3% vs. 4.0–4.6%). Of top-PLG alprazolam- and opioid-treated patients, respectively, 20.7 and 9.9% developed incident (new) Lorenz-1 in followup, compared with < 3% of Lorenz-25 patients. For gabapentin, neither PLA nor LCA predicted incident Lorenz-1 (PPV = 0.0–1.4%). For all three medications, PLA sensitivity for follow-up hospitalization was < 5%, but specificity was better for PLA (97.3–99.2%) than for LCA (74.3–75.4%). PLA better identified patients at risk of future controlled substance abuse/diversion than did LCA, but the technique needs refinement before widespread use