Price transparency at the point of prescribing with real-time prescription benefits.

PURPOSE: Real-time prescription benefits (RTPB) shows prescribers patient-, medication-, and pharmacy-specific information on medication pricing, prior authorization requirements, and lower-cost alternatives. RTPB is intended to improve patient satisfaction and prescription fill rates by decreasing out-of-pocket costs for prescriptions. Therefore, we evaluated how RTPB affects prescribing patterns by examining acceptance and subsequent fill rates for RTPB alternative suggestions. METHODS: RTPB was implemented in February 2022 using external vendor interfaces. Prescribing data from March 2022 to March 2023 were analyzed. RTPB displayed alerts for medications requiring prior authorization or when alternative medications would result in cost savings. Patients were included if their prescription received an RTPB response and they had a subsequent encounter with pharmacy fill data. Primary outcomes were alert acceptance rates and prescription fill rates across RTPB alert groups, with a secondary outcome of monthly copay savings for accepted alerts. RESULTS: RTPB requests received a response for 88% of prescriptions, with price estimates provided for 77.9% of them. Lower-cost alternatives accounted for 67.2% of alerts, while prior authorization requirements represented 15% of alerts. Prescribers selected a lower-cost alternative 32% of the time. For those with an RTPB alert, patients filled prescriptions 68% of the time when an alternative was chosen, compared to 59% of the time when the original prescription was retained (odds ratio, 1.5; 95% confidence interval, 1.5-1.6; P < 0.001). Patients saved an average of $27.77 per month on copay costs when alternatives were selected. CONCLUSION: Implementation of RTPB was found to result in significant improvements in prescription fill rates and decrease patient copay costs, despite low alert acceptance rates.

Improving Medication Management for Inpatients with a Secondary Diagnosis of Parkinson Disease.

ABSTRACT: Patients who have Parkinson disease require individualized medication regimens to optimize care. A review of the medication management of patients admitted to a tertiary care hospital with a secondary diagnosis of Parkinson disease found significant departures from the patients' home regimen. Medication regimens are often altered by health care teams unfamiliar with Parkinson disease-specific care in order to conform to standard hospital medication orders and administration times, potentially resulting in increased patient falls, delirium, and mortality.A nurse-led multidisciplinary team consisting of pharmacy, nursing, informatics, neurology, and quality personnel implemented a quality improvement (QI) project between July 2020 and July 2022 to identify patients with Parkinson disease, including those with a secondary diagnosis and those undergoing deep brain stimulation, and customize medication management in order to reduce length of stay, mortality, falls, falls with harm, and 30-day readmissions. The QI project team also evaluated patient satisfaction with medication management.Among patients with a secondary diagnosis of Parkinson disease, the proportion who had medication histories conducted by a pharmacy staff member increased from a baseline of 53% to more than 75% per month. For all patients with Parkinson disease, those whose medication history was taken by a pharmacy staff member had orders matching their home regimen 89% of the time, whereas those who did not had orders matching the home regimen only 40% of the time. Among patients with a secondary diagnosis of Parkinson disease, the length-of-stay index decreased from a baseline of 1 to 0.94 and observed-to-expected mortality decreased from 1.03 to 0.78. The proportion of patients experiencing a fall decreased from an average of 5% to 4.08% per quarter, while the proportion of patients experiencing a fall with harm decreased from an average of 1% to 0.75% per quarter. The rate of 30-day readmissions decreased from 10.81% to 4.53% per quarter. Patient satisfaction scores were 1.95 points higher for patients who had medication histories taken by pharmacy than for those who did not (5 versus 3.05).

The Impact of Clinical Decision Support on Health Disparities and the Digital Divide.

OBJECTIVES: This literature review summarizes relevant studies from the last three years (2020-2022) related to clinical decision support (CDS) and CDS impact on health disparities and the digital divide. This survey identifies current trends and synthesizes evidence-based recommendations and considerations for future development and implementation of CDS tools. METHODS: We conducted a search in PubMed for literature published between 2020 and 2022. Our search strategy was constructed as a combination of the MEDLINE®/PubMed® Health Disparities and Minority Health Search Strategy and relevant CDS MeSH terms and phrases. We then extracted relevant data from the studies, including priority population when applicable, domain of influence on the disparity being addressed, and the type of CDS being used. We also made note of when a study discussed the digital divide in some capacity and organized the comments into general themes through group discussion. RESULTS: Our search yielded 520 studies, with 45 included at the conclusion of screening. The most frequent CDS type in this review was point-of-care alerts/reminders (33.3%). Health Care System was the most frequent domain of influence (71.1%), and Blacks/African Americans were the most frequently included priority population (42.2%). Throughout the literature, we found four general themes related to the technology divide: inaccessibility of technology, access to care, trust of technology, and technology literacy.This survey revealed the diversity of CDS being used to address health disparities and several barriers which may make CDS less effective or potentially harmful to certain populations. Regular examinations of literature that feature CDS and address health disparities can help to reveal new strategies and patterns for improving healthcare.

Assessment of a Naloxone Coprescribing Alert for Patients at Risk of Opioid Overdose: A Quality Improvement Project.

BACKGROUND: Patients taking high doses of opioids, or taking opioids in combination with other central nervous system depressants, are at increased risk of opioid overdose. Coprescribing the opioid-reversal agent naloxone is an essential safety measure, recommended by the surgeon general, but the rate of naloxone coprescribing is low. Therefore, we set out to determine whether a targeted clinical decision support alert could increase the rate of naloxone coprescribing. METHODS: We conducted a before-after study from January 2019 to April 2021 at a large academic health system in the Southeast. We developed a targeted point of care decision support notification in the electronic health record to suggest ordering naloxone for patients who have a high risk of opioid overdose based on a high morphine equivalent daily dose (MEDD) ≥90 mg, concomitant benzodiazepine prescription, or a history of opioid use disorder or opioid overdose. We measured the rate of outpatient naloxone prescribing as our primary measure. A multivariable logistic regression model with robust variance to adjust for prescriptions within the same prescriber was implemented to estimate the association between alerts and naloxone coprescribing. RESULTS: The baseline naloxone coprescribing rate in 2019 was 0.28 (95% confidence interval [CI], 0.24-0.31) naloxone prescriptions per 100 opioid prescriptions. After alert implementation, the naloxone coprescribing rate increased to 4.51 (95% CI, 4.33-4.68) naloxone prescriptions per 100 opioid prescriptions (P < .001). The adjusted odds of naloxone coprescribing after alert implementation were approximately 28 times those during the baseline period (95% CI, 15-52). CONCLUSIONS: A targeted decision support alert for patients at risk for opioid overdose significantly increased the rate of naloxone coprescribing and was relatively easy to build.

Nonoperative management of adhesive small bowel obstruction: what is the break point?

BACKGROUND: The current management paradigm for recurrent adhesive small bowel obstruction (SBO) is nonoperative. Rates of recurrence differ based on time interval between and number of previous occurrences. Optimal time to intervene has not been determined. METHODS: We constructed a Markov model to evaluate costs and quality of life on a hypothetical cohort of 40-year-old patients after their first episode of medical management for postoperative SBO. We estimated a relative risk reduction of .55 with surgical intervention and a relative risk increase of 2.1, 2.9, and 5.7 after the medical management of the 2nd, 3rd, and 4th SBO. RESULTS: Surgery performed after earlier episodes of SBO was more costly but also more effective. The cost difference between surgery after the 1st SBO recurrence vs the 2nd SBO recurrence was $1,643, with an increase of .135 quality-adjusted life years (QALYs), the incremental cost-effectiveness ratio was $12,170 per QALY. CONCLUSIONS: Surgery after the first episode of SBO provides a small increase in QALY at a small cost since surgical intervention lowers the risk of recurrence.

Costs and Mortality Associated With Multidrug-Resistant Healthcare-Associated Acinetobacter Infections.

BACKGROUND Our objective was to estimate the per-infection and cumulative mortality and cost burden of multidrug-resistant (MDR) Acinetobacter healthcare-associated infections (HAIs) in the United States using data from published studies. METHODS We identified studies that estimated the excess cost, length of stay (LOS), or mortality attributable to MDR Acinetobacter HAIs. We generated estimates of the cost per HAI using 3 methods: (1) overall cost estimates, (2) multiplying LOS estimates by a cost per inpatient-day ($4,350) from the payer perspective, and (3) multiplying LOS estimates by a cost per inpatient-day from the hospital ($2,030) perspective. We deflated our estimates for time-dependent bias using an adjustment factor derived from studies that estimated attributable LOS using both time-fixed methods and either multistate models (70.4% decrease) or matching patients with and without HAIs using the timing of infection (47.4% decrease). Finally, we used the incidence rate of MDR Acinetobacter HAIs to generate cumulative incidence, cost, and mortality associated with these infections. RESULTS Our estimates of the cost per infection were $129,917 (method 1), $72,025 (method 2), and $33,510 (method 3). The pooled relative risk of mortality was 4.51 (95% CI, 1.10-32.65), which yielded a mortality rate of 10.6% (95% CI, 2.5%-29.4%). With an incidence rate of 0.141 (95% CI, 0.136-0.161) per 1,000 patient-days at risk, we estimated an annual cumulative incidence of 12,524 (95% CI, 11,509-13,625) in the United States. CONCLUSION The estimates presented here are relevant to understanding the expenditures and lives that could be saved by preventing MDR Acinetobacter HAIs. Infect Control Hosp Epidemiol 2016;1-7.

Calculating the Baseline Incidence in Patients Without Risk Factors: A Strategy for Economic Evaluation.

Economic and epidemiological models need various inputs to estimate the occurrence of events in different subsets of the population, such as the incidence of events for patients with risk factors compared with those without. However, the baseline event incidence for patients without risk factors (incidence_no_risk) may not be reported in the literature, therefore the event incidence in the population (incidence_pop) is commonly used in its place as the baseline. However, this is problematic because incidence_pop is a weighted average of a heterogeneous population. We therefore developed a method for deriving the incidence for persons without risk factors (incidence_no_risk) by adjustment of incidence_pop. We calculated incidence_no_risk using the relative risk for events due to risk factors (RR_risk), incidence_pop, and the prevalence of the risk factor (pRF), which are typically available in the literature. Since the incidence for patient with risk factors (incidence_risk) can be expressed as incidence_risk = incidence_no_risk × RR_risk, we found that incidence_no_risk = incidence_pop/((RR_risk × pRF) + (1 - pRF)). We validated the equation by modeling the fracture incidence in high-risk patients in an osteoporosis transition-state model. With incidence_pop used as the baseline fracture incidence, the model overestimated hip fractures in the study population (10.72 fractures/1000 patient-years). After adjustment of incidence_pop using incidence_no_risk as the baseline incidence, the model accurately predicted hip fractures (2.27/1000 patient-years). Therefore, incidence_no_risk can be calculated using this method based on the event incidence for the study population, the relative risk increase associated with the risk factor, and the prevalence of the risk factor.

An economic analysis of adherence engineering to improve use of best practices during central line maintenance procedures.

OBJECTIVE: Adherence engineering applies human factors principles to examine non-adherence within a specific task and to guide the development of materials or equipment to increase protocol adherence and reduce human error. Central line maintenance (CLM) for intensive care unit (ICU) patients is a task through which error or non-adherence to protocols can cause central line-associated bloodstream infections (CLABSIs). We conducted an economic analysis of an adherence engineering CLM kit designed to improve the CLM task and reduce the risk of CLABSI. METHODS: We constructed a Markov model to compare the cost-effectiveness of the CLM kit, which contains each of the 27 items necessary for performing the CLM procedure, compared with the standard care procedure for CLM, in which each item for dressing maintenance is gathered separately. We estimated the model using the cost of CLABSI overall ($45,685) as well as the excess LOS (6.9 excess ICU days, 3.5 excess general ward days). RESULTS: Assuming the CLM kit reduces the risk of CLABSI by 100% and 50%, this strategy was less costly (cost savings between $306 and $860) and more effective (between 0.05 and 0.13 more quality-adjusted life-years) compared with not using the pre-packaged kit. We identified threshold values for the effectiveness of the kit in reducing CLABSI for which the kit strategy was no longer less costly. CONCLUSION: An adherence engineering-based intervention to streamline the CLM process can improve patient outcomes and lower costs. Patient safety can be improved by adopting new approaches that are based on human factors principles.

National evaluation of prescriber drug dispensing.

OBJECTIVE: To describe the legal, professional, and consumer status of prescribers dispensing legend and over-the-counter drugs in the United States. METHODS: Legal and academic databases were searched to identify those states that permit prescribers to dispense medications to patients and any limitations on such practice. In addition, prescribers and patients-consumers were surveyed to learn about the prevalence and perceptions of such practice. The use of drug samples was explicitly excluded from the study. MAIN RESULTS: Surveys were obtained from 556 physicians, 64 NPs, and 999 patient-consumers of drugs dispensed by prescribers. Forty-four states authorize prescriber dispensing. Midlevel practitioners (i.e., NPs and physician assistants) are authorized to dispense in 43 states. Thirty-two states do not require dispensing prescribers to compete additional registration to dispense medications, and 30 states require some level of compliance with pharmacy practice requirements. Prescriber dispensing is common, independent of patient age or insurance coverage. Prescriber dispensing appears driven by physician and patient perceptions of convenience and cost reductions. Future dispensing is likely to increase due to consumers' satisfaction with the practice. Consumer self-reported adverse drug reactions (ADRs) were equivalent between pharmacist- and physician-dispensed drugs, but urgent and emergency clinic ADR consultations were slightly lower with physician dispensing. CONCLUSIONS: Prescriber dispensing is firmly entrenched in the U.S. health care system, is likely to increase, does not appear to increase ADRs, and may reduce urgent care and emergency department visits. The reduction in urgent care and emergency department visits requires further study to confirm these preliminary findings.