Advancing Interoperability and Prior Authorization Reform.

This Viewpoint discusses the provisions and potential of the new Centers for Medicare and Medicaid Services (CMS) Interoperability and Prior Authorization Final Rule.

Does Insulin Price Reduction Suggest a Path Forward for Lowering Drug Costs?

This Viewpoint investigates whether political pressure such as that used to decrease insulin pricing is a feasible method of lowering the cost of other drugs.

The Settlement of the Blue Cross Blue Shield Antitrust Litigation: Creating a New Potential Catalyst for Health Insurance Industry Restructuring.

Importance: The recent settlement of the class action antitrust lawsuit against the Blue Cross Blue Shield (BCBS) Association and 34 plans will bring substantial change to insurance markets in the US; however, not enough is known about the implications for insurance markets and health policy. Objective: To detail the nature of the antitrust claims against the BCBS plans and their Association, and the required changes in their business practices that are a critical part of the settlement. Evidence Review: This analysis relies on the court documents that will set the new parameters under which BCBS plans must operate. Findings: For many of the past 70 years, BCBS plans have enjoyed substantial market power in the state in which each operates. Other BCBS plans were not allowed to compete against other "Blues" plans, and any corporation resident in a plan's territory was allowed to deal solely with the local plan if the corporation sought BCBS insurance for its employees. The settlement recognizes that these restrictions reduced competition; therefore, they are not allowed under federal antitrust laws going forward. The BCBS Association also will have less control over mergers and acquisitions by individual BCBS plans. Finally, the BCBS plans and the Association must pay $2.67 billion in damages to clients. Conclusions and Relevance: These findings suggest that BCBS plans will face competition from their fellow plans for business, and they will be allowed to diversify the services they provide. These changes will be likely to threaten the smaller BCBS plans and could bring consolidation to the health insurance industry. These changes should also promote sharper efforts toward health care cost management, with broad implications for other parts of the health insurance industry, and for hospitals and other health care services.

Coverage Denials: Government And Private Insurer Policies For Medical Necessity In Medicare.

Little is publicly known about coverage denials for medical services that do not meet medical necessity criteria. We characterized the extent of these denials and their key features, using Medicare Advantage claims for a large insurer from the period 2014-19. In this setting, claims could be denied because of traditional Medicare's coverage rules or additional Medicare Advantage private insurer rules. We observed $416 million in denied spending, with 0.81 denials and $60 of denied spending per beneficiary annually. We found that 1.40 percent of services were denied and 0.68 percent of total spending was denied, with rates rising over time. Traditional Medicare's coverage rules accounted for 85 percent of denied services and 64 percent of denied spending; the remaining denials were due to additional Medicare Advantage insurer rules. Denial rates varied greatly across service type and provider type, with the most denials being for laboratory services and hospital outpatient providers. Traditional Medicare and Medicare Advantage insurer coverage policies each addressed different sources of medical spending; together they contributed to the denial of a modest but nontrivial portion of payments.

Health Care Utilization and Spending in Medicare Advantage vs Traditional Medicare: A Difference-in-Differences Analysis.

Importance: Medicare Advantage (MA) has entailed a major expansion of government-financed, privately administered health insurance in the US. As policy makers consider options to expand Medicare further, it is informative to compare the performance of traditional Medicare (TM) and MA. Objective: To assess whether MA is associated with differential changes in health care utilization and spending for beneficiaries entering Medicare from commercial insurance compared with beneficiaries entering TM. Design Setting and Participants: This retrospective cohort study with a difference-in-differences analysis and propensity score matching compared health care utilization and spending between beneficiaries enrolling in MA and beneficiaries enrolling in TM with a Medicare Supplement plan 1 year before vs 1 year after their initial Medicare enrollment. Participants included beneficiaries aged 65 to 70 years who remained enrolled with a large insurer when transitioning from commercial insurance to Medicare between June 2018 and December 2018. Data were analyzed from February 2020 to October 2021. Main Outcomes and Measures: Use of, and spending on, institutional (Part A) and professional (Part B) medical services, measured as overall spending per member per month, and as rates of services per thousand members per year, including inpatient stays, inpatient days, physician visits, and injectable drug administrations. Results: Among 1082 matched beneficiaries (541 joining MA, 541 joining TM with a Supplement plan), 585 (54.1%) were female, and the mean (SD) age at Medicare enrollment was 66 (1.4) years. Prior to Medicare enrollment, there was no statistically significant difference in outcome trends between the MA and TM groups. The first year of MA enrollment was associated with a differential reduction in institutional (Part A) spending of $95 (95% CI, $7-$183) per member per month, corresponding to a differential reduction in inpatient stays of 63 (95% CI, 10-116) per thousand members per year. Medicare Advantage was associated with a differential reduction in total spending (Parts A and B) of $142 (95% CI, $0-$282) per member per month, which was 36% of total spending in TM. There was no differential reduction in professional (Part B) spending (per member per month, $47; 95% CI, $51-$145) or utilization. Conclusions and Relevance: In this cohort study with a difference-in-differences analysis, during the first year of Medicare coverage, MA was associated with large reductions in institutional (Part A) utilization and spending.

Measuring the Scope of Prior Authorization Policies: Applying Private Insurer Rules to Medicare Part B.

Importance: Health insurers use prior authorization to evaluate the medical necessity of planned medical services. Data challenges have precluded measuring the frequency with which medical services can require prior authorization, the spending on these services, the types of services and clinician specialties affected, and differences in the scope of prior authorization policies between government-administered and privately administered insurance. Objectives: To measure the extent of prior authorization requirements for medical services and to describe the services and clinician specialties affected by them using novel data on private insurer coverage policies. Design Setting and Participants: Fee-for-service Medicare claims from 2017 were analyzed for beneficiaries in Medicare Part B, which lacks prior authorization. We measured the use of services that would have been subject to prior authorization according to the coverage rules of a large Medicare Advantage insurer and calculated the associated spending. We report the rates of these services for 14 clinical categories and 27 clinician specialties. Main Outcomes and Measures: Annual count per beneficiary and associated spending for 1151 services requiring prior authorization by the Medicare Advantage insurer; likelihood of providing 1 or more such service per year, by clinician specialty. Results: Of 6 497 534 beneficiaries (mean [SD] age, 72.1 [12.1] years), 41% received at least 1 service per year that would have been subject to prior authorization under Medicare Advantage prior authorization requirements. The mean (SD) number of services per beneficiary per year was 2.2 (8.9) (95% CI, 2.17-2.18), corresponding to a mean (SD) of $1661 ($8900) in spending per beneficiary per year (95% CI, $1654-$1668), or 25% of total annual Part B spending. Part B drugs constituted 58% of the associated spending, mostly accounted for by hematology or oncology drugs. Radiology was the largest source of nondrug spending (16%), followed by musculoskeletal services (9%). Physician specialties varied widely in rates of services that required prior authorization, with highest rates among radiation oncologists (97%), cardiologists (93%), and radiologists (91%) and lowest rates among pathologists (2%) and psychiatrists (4%). Conclusions and Relevance: In this cross-sectional study, a large portion of fee-for-service Medicare Part B spending would have been subject to prior authorization under private insurance coverage policies. Prior authorization requirements for Part B drugs have been an important source of difference in coverage policy between government-administered and privately administered Medicare.

Effect of Patient Financial Incentives on Statin Adherence and Lipid Control: A Randomized Clinical Trial.

Importance: Financial incentives can improve medication adherence and cardiovascular disease risk, but the optimal design to promote sustained adherence after incentives are discontinued is unknown. Objective: To determine whether 6-month interventions involving different financial incentives to encourage statin adherence reduce low-density lipoprotein cholesterol (LDL-C) levels from baseline to 12 months. Design, Setting, and Participants: This 4-group, randomized clinical trial was conducted from August 2013 to July 2018 among several large US insurer or employer populations and the University of Pennsylvania Health System. The study population included adults with elevated risk of cardiovascular disease, suboptimal LDL-C control, and evidence of imperfect adherence to statin medication. Data analysis was performed from July 2017 to June 2019. Interventions: The interventions lasted 6 months during which all participants received daily medication reminders and an electronic pill bottle. Statin adherence was measured by opening the bottle. For participants randomized to the 3 intervention groups, adherence was rewarded with financial incentives. The sweepstakes group involved incentives for daily adherence. In the deadline sweepstakes group, incentives were reduced if participants were adherent only after a reminder. The sweepstakes plus deposit contract group split incentives between daily adherence and a monthly deposit reduced for each day of nonadherence. Main Outcomes and Measures: The primary outcome was change in LDL-C level from baseline to 12 months. Results: Among 805 participants randomized (199 in the simple daily sweepstakes group, 204 in the deadline sweepstakes group, 201 in the sweepstakes plus deposit contract group, and 201 in the control group), the mean (SD) age was 58.5 (10.3) years; 519 participants (64.5%) were women, 514 (63.9%) had diabetes, and 273 (33.9%) had cardiovascular disease. The mean (SD) baseline LDL-C level was 143.2 (42.5) mg/dL. Measured adherence at 6 months (defined as the proportion of 180 days with electronic pill bottle opening) in the control group (0.69; 95% CI, 0.66-0.72) was lower than that in the simple sweepstakes group (0.84; 95% CI, 0.81-0.87), the deadline sweepstakes group (0.86; 95% CI, 0.83-0.89), and the sweepstakes plus deposit contract group (0.87; 95% CI, 0.84-0.90) (P < .001 for each incentive group vs control). LDL-C levels were measured for 636 participants at 12 months. Mean LDL-C level reductions from baseline to 12 months were 33.6 mg/dL (95% CI, 28.4-38.8 mg/dL) in the control group, 32.4 mg/dL (95% CI, 27.3-37.6 mg/dL) in the sweepstakes group, 33.2 mg/dL (95% CI, 28.1-38.3 mg/dL) in the deadline sweepstakes group, and 36.5 mg/dL (95% CI, 31.3-41.7 mg/dL) in the sweepstakes plus deposit contract group (adjusted P > .99 for each incentive group vs control). Conclusions and Relevance: Compared with the control group, different financial incentives improved measured statin adherence but not LDL-C levels. This result points to the importance of directly measuring health outcomes, rather than simply adherence, in trials aimed at improving health behaviors. Trial Registration: ClinicalTrials.gov Identifier: NCT01798784.

Aetna's Compassionate Care Program: Sustained Value for Our Members with Advanced Illness.

Background: In 2004, Aetna, a national health insurer, launched the Aetna Compassionate Care Program (ACCP) targeting members diagnosed with an advanced illness with a view to increase access to palliative care and hospice services. Objective: The objective of this study is to evaluate the impact of ACCP on health care utilization and hospice enrollment among enrolled members. Methods: This was a retrospective cohort study comparing participants in ACCP to a matched control group using a propensity score method. The study group consisted of Aetna Medicare Advantage members who participated in the ACCP between January 2014 and June 2015. Potential control group members were those who were not identified by the predictive model nor were referred to the ACCP program through other means. The primary outcomes of interest were hospice use measured as percent of members electing hospice and median number of days in hospice; health care utilization and medical costs measured as rates and medical costs associated with acute inpatient admissions, emergency room, primary care, and specialty visits in the 30 and 90 days before death. Results: Participants in the ACCP program were 36% more likely to enroll in hospice (79% vs. 58%, p < 0.0001) and had reduced acute inpatient medical costs ($4169 vs. $5863, p < 0.0001) driven primarily by fewer inpatient admissions (860 vs. 1017, p < 0.0001) in the last 90 days of life. Conclusions: Advanced illness case management programs such as ACCP can improve access to hospice and improve patient outcomes while reducing unnecessary admissions in the last 90 days of life.

Advancing the Science of Healthcare Service Delivery: The NHLBI Corporate Healthcare Leaders' Panel.

There is a growing gap between available science and evidence and the ability of service providers to deliver high-quality care in a cost-effective way to the entire population. We believe that the chasm between knowledge and action is due to a lack of concerted effort among all organizations that deliver health care services across the life span of patients. Broad participation is needed and necessitates a far more explicit and concerted public-private partnership focused on large-scale transformation. In this context, the National Heart, Lung, and Blood Institute convened a panel made up of leaders of corporate health care entities, including academic health centers, and government agency representatives to inform contemporary strategic partnerships with health care companies. This article provides insights from the meeting on how to execute a transformative innovation research agenda that will foster improvements in health care service delivery by leveraging the translation of biomedical research evidence in real-world settings.

Partners and Alerts in Medication Adherence: A Randomized Clinical Trial.

BACKGROUND: Poor medication adherence is common and limits the effectiveness of treatment. OBJECTIVE: To investigate how social supports, automated alerts, and their combination improve medication adherence. DESIGN: Four-arm, randomized clinical trial with a 6-month intervention. PARTICIPANTS: A total of 179 CVS health employees or adult dependents with CVS Caremark prescription coverage, a current daily statin prescription, a medication possession ratio less than 80%, and Internet access. INTERVENTIONS: Participants were randomly assigned to control, social support (partner), automated adherence alert messages (alert), or both social support and alerts (partner + alert). Participants in the social support arms were asked to name a medication adherence partner (MAP) to help them take their medication. Participants in the alert arms were sent emails, text messages, or automated phone calls if they had failed to adhere on the previous day and on one or both of the 2 days before that. In partner + alert, both participants and fully enrolled MAPs received alerts. MAIN MEASURES: Adherence measured by wireless pill bottle opening. KEY RESULTS: Compared to 36.0% adherence in control, adherence was significantly greater in the alert arm (52.9%, difference vs. control of 17.0%, 95% CI for difference 6.3 to 27.6%, P = 0.002) and the partner + alert arm (54.5%, difference vs. control of 18.6%, 95% CI for difference 6.6 to 30.5%, P = 0.003). Adherence in the partner arm was not statistically significantly greater than control (43.2%, difference vs. control of 7.2%, 95% CI of difference - 5.2% to 19.5%, P = 0.25). There were no statistically significant differences among the three treatment arms. Fewer participants invited a MAP in the partner + alert arm than the partner arm (P = 0.02). CONCLUSIONS: Automated alerts were effective at improving medication adherence. Assigning a medication adherence partner did not statistically significantly affect adherence rates. TRIAL REGISTRATION: ClinicalTrials.gov Number NCT01890018 [ https://clinicaltrials.gov /].

Automatable algorithms to identify nonmedical opioid use using electronic data: a systematic review.

OBJECTIVE: Improved methods to identify nonmedical opioid use can help direct health care resources to individuals who need them. Automated algorithms that use large databases of electronic health care claims or records for surveillance are a potential means to achieve this goal. In this systematic review, we reviewed the utility, attempts at validation, and application of such algorithms to detect nonmedical opioid use. MATERIALS AND METHODS: We searched PubMed and Embase for articles describing automatable algorithms that used electronic health care claims or records to identify patients or prescribers with likely nonmedical opioid use. We assessed algorithm development, validation, and performance characteristics and the settings where they were applied. Study variability precluded a meta-analysis. RESULTS: Of 15 included algorithms, 10 targeted patients, 2 targeted providers, 2 targeted both, and 1 identified medications with high abuse potential. Most patient-focused algorithms (67%) used prescription drug claims and/or medical claims, with diagnosis codes of substance abuse and/or dependence as the reference standard. Eleven algorithms were developed via regression modeling. Four used natural language processing, data mining, audit analysis, or factor analysis. DISCUSSION: Automated algorithms can facilitate population-level surveillance. However, there is no true gold standard for determining nonmedical opioid use. Users must recognize the implications of identifying false positives and, conversely, false negatives. Few algorithms have been applied in real-world settings. CONCLUSION: Automated algorithms may facilitate identification of patients and/or providers most likely to need more intensive screening and/or intervention for nonmedical opioid use. Additional implementation research in real-world settings would clarify their utility.

Home infusion: Safe, clinically effective, patient preferred, and cost saving.

BACKGROUND: As the U.S. healthcare payment system shifts from volume to value, identifying care approaches that improve outcomes while lowering costs are essential. We sought to understand the utility of home infusion versus medical-setting infusion as a mechanism to affect the three-part aim: better care, better health outcomes, and lower costs. STUDY DESIGN: Systematic review. METHODS: We searched MEDLINE, EMBASE, and Science Citation Index for articles related to the safety, clinical effectiveness, quality of life and satisfaction, and/or costs of home infusion as compared with infusion in an outpatient medical facility or hospital. RESULTS: Of 253 potentially relevant articles, 13 met all inclusion criteria. Study design, disease state, and outcomes varied considerably. As compared to medical setting infusion patients, home infusion patients were no more likely to experience adverse drug events or side effects (all p>0.05). Clinical outcomes were as good or better, e.g., for patients with hemophilia, a 40% (0.50-0.70) reduced likelihood of hospitalization for bleeding complications. Patients overwhelmingly preferred home infusion, reporting significantly better physical and mental well being and less disruption of family and personal responsibilities. Home infusion costs were significantly lower than medical setting infusion costs, with savings between $1928 and $2974 per treatment course. CONCLUSIONS: Home infusion care can provide safe, clinically effective care improve patients' quality of life and reduce healthcare costs. As the overhaul of the healthcare payment system gains momentum, the home infusion care delivery model offers strong promise as one in a set of approaches that can improve care and lower costs.

Association Between Academic Medical Center Pharmaceutical Detailing Policies and Physician Prescribing.

IMPORTANCE: In an effort to regulate physician conflicts of interest, some US academic medical centers (AMCs) enacted policies restricting pharmaceutical representative sales visits to physicians (known as detailing) between 2006 and 2012. Little is known about the effect of these policies on physician prescribing. OBJECTIVE: To analyze the association between detailing policies enacted at AMCs and physician prescribing of actively detailed and not detailed drugs. DESIGN, SETTING, AND PARTICIPANTS: The study used a difference-in-differences multivariable regression analysis to compare changes in prescribing by physicians before and after implementation of detailing policies at AMCs in 5 states (California, Illinois, Massachusetts, Pennsylvania, and New York) that made up the intervention group with changes in prescribing by a matched control group of similar physicians not subject to a detailing policy. EXPOSURES: Academic medical center implementation of policies regulating pharmaceutical salesperson visits to attending physicians. MAIN OUTCOMES AND MEASURES: The monthly within-drug class market share of prescriptions written by an individual physician for detailed and nondetailed drugs in 8 drug classes (lipid-lowering drugs, gastroesophageal reflux disease drugs, diabetes drugs, antihypertensive drugs, hypnotic drugs approved for the treatment of insomnia [sleep aids], attention-deficit/hyperactivity disorder drugs, antidepressant drugs, and antipsychotic drugs) comparing the 10- to 36-month period before implementation of the detailing policies with the 12- to 36-month period after implementation, depending on data availability. RESULTS: The analysis included 16 121 483 prescriptions written between January 2006 and June 2012 by 2126 attending physicians at the 19 intervention group AMCs and by 24 593 matched control group physicians. The sample mean market share at the physician-drug-month level for detailed and nondetailed drugs prior to enactment of policies was 19.3% and 14.2%, respectively. Exposure to an AMC detailing policy was associated with a decrease in the market share of detailed drugs of 1.67 percentage points (95% CI, -2.18 to -1.18 percentage points; P < .001) and an increase in the market share of nondetailed drugs of 0.84 percentage points (95% CI, 0.54 to 1.14 percentage points; P < .001). Associations were statistically significant for 6 of 8 study drug classes for detailed drugs (lipid-lowering drugs, gastroesophageal reflux disease drugs, antihypertensive drugs, sleep aids, attention-deficit/hyperactivity disorder drugs, and antidepressant drugs) and for 9 of the 19 AMCs that implemented policies. Eleven of the 19 AMCs regulated salesperson gifts to physicians, restricted salesperson access to facilities, and incorporated explicit enforcement mechanisms. For 8 of these 11 AMCs, there was a significant change in prescribing. In contrast, there was a significant change at only 1 of 8 AMCs that did not enact policies in all 3 areas. CONCLUSIONS AND RELEVANCE: Implementation of policies at AMCs that restricted pharmaceutical detailing between 2006 and 2012 was associated with modest but significant reductions in prescribing of detailed drugs across 6 of 8 major drug classes; however, changes were not seen in all of the AMCs that enacted policies.

Predicting 1-Year Statin Adherence Among Prevalent Users: A Retrospective Cohort Study.

BACKGROUND: Attempts to predict who is at risk of future nonadherence have largely focused on predictions at the time of therapy initiation; however, these users are only a small proportion of all patients on therapy at any point in time. Methods to predict nonadherence for established medication users, which have not been previously described in the literature, would be helpful to guide efforts to enhance the use of evidence-based therapies. OBJECTIVE: To test approaches for adherence prediction among prevalent statin users, namely the use of short-term filling behavior, investigator-specified predictors from medical and pharmacy administrative claims, and the empirical selection of potential predictors using the high-dimensional propensity score variable selection algorithm. METHODS: Medical and prescription claims data from a large national health insurer were used to create a cohort of patients who filled statin medication prescriptions in January 2012. We defined 6 groups of adherence predictors and estimated 10 main models to predict medication adherence in the full cohort. The same was done for the population stratified based on the days supply of the index statin prescription (≤ 30 days vs. > 30 days). RESULTS: The study cohort consisted of 93,777 individuals, 58.4% of which were adherent to statins during follow-up. The use of 3 pre-index adherence predictors alone achieved a c-statistic of 0.70. Investigator-specified and empirically selected pharmacy, medical, and demographic variables did substantially worse (0.57-0.60). The use of 3 indicators of post-index adherence achieved a higher c-statistic than the best-performing model using pre-index information (0.74 vs. 0.72). The addition of 3 pre-index adherence predictors further improved discrimination (0.78). CONCLUSIONS: This analysis demonstrated the ability to predict adherence among medication users using filling behavior before and immediately after an index prescription fill. DISCLOSURES: This work was supported by an unrestricted grant from CVS Health to Brigham and Women's Hospital. Shrank, Brennan, and Matlin were employees and shareholders at CVS Health at the time of this manuscript preparation; they report no financial interests in products or services that are related to the subject of the manuscript. Franklin has received consulting fees from Aetion. Chourdry has received grants from the National Heart, Lung, and Blood Institute, PhRMA Foundation, Merck, Sanofi, AstraZeneca, and MediSafe. Spettell is an employee of, and shareholder in, Aetna. The other authors have nothing to disclose. Krumme, Choudhry, Tong, and Franklin contributed to the study design, interpretation of results, and manuscript drafting. Tong prepared and analyzed the data. Isaman, Spettell, Shrank, Brennan, and Matlin provided interpretation of results and critical manuscript revisions.

Effect of Reminder Devices on Medication Adherence: The REMIND Randomized Clinical Trial.

Importance: Forgetfulness is a major contributor to nonadherence to chronic disease medications and could be addressed with medication reminder devices. Objective: To compare the effect of 3 low-cost reminder devices on medication adherence. Design, Setting, and Participants: This 4-arm, block-randomized clinical trial involved 53 480 enrollees of CVS Caremark, a pharmacy benefit manager, across the United States. Eligible participants were aged 18 to 64 years and taking 1 to 3 oral medications for long-term use. Participants had to be suboptimally adherent to all of their prescribed therapies (with a medication possession ratio of 30% to 80%) in the 12 months before randomization. Participants were stratified on the basis of the medications they were using at randomization: medications for cardiovascular or other nondepression chronic conditions (the chronic disease stratum) and antidepressants (the antidepressant stratum). In each stratum, randomization occurred within blocks defined by whether all of the patient's targeted medications were dosed once daily. Patients were randomized to receive in the mail a pill bottle strip with toggles, digital timer cap, or standard pillbox. The control group received neither notification nor a device. Data were collected from February 12, 2013, through March 21, 2015, and data analyses were on the intention-to-treat population. Main Outcomes and Measures: The primary outcome was optimal adherence (medication possession ratio ≥80%) to all eligible medications among patients in the chronic disease stratum during 12 months of follow-up, ascertained using pharmacy claims data. Secondary outcomes included optimal adherence to cardiovascular medications among patients in the chronic disease stratum as well as optimal adherence to antidepressants. Results: Of the 53 480 participants, mean (SD) age was 45 (12) years and 56% were female. In the primary analysis, 15.5% of patients in the chronic disease stratum assigned to the standard pillbox, 15.1% assigned to the digital timer cap, 16.3% assigned to the pill bottle strip with toggles, and 15.1% assigned to the control arm were optimally adherent to their prescribed treatments during follow-up. There was no statistically significant difference in the odds of optimal adherence between the control and any of the devices (standard pillbox: odds ratio [OR], 1.03 [95% CI, 0.95-1.13]; digital timer cap: OR, 1.00 [95% CI, 0.92-1.09]; and pill bottle strip with toggles: OR, 0.94 [95% CI, 0.85-1.04]). In direct comparisons, the odds of optimal adherence were higher with a standard pillbox than with the pill bottle strip (OR, 1.10 [95% CI, 1.00-1.21]). Secondary analyses yielded similar results. Conclusions and Relevance: Low-cost reminder devices did not improve adherence among nonadherent patients who were taking up to 3 medications to treat common chronic conditions. The devices may have been more effective if coupled with interventions to ensure consistent use or if targeted to individuals with an even higher risk of nonadherence. Trial Registration: clinicaltrials.gov Identifier: NCT02015806.

Impact of CVS Pharmacy's Discontinuance of Tobacco Sales on Cigarette Purchasing (2012-2014).

OBJECTIVES: To assess the impact of CVS Health's discontinuation of tobacco sales on cigarette purchasing. METHODS: We used households' purchasing data to assess rates at which households stopped cigarette purchasing for at least 6 months during September 2014 to August 2015 among 3 baseline groups: CVS-exclusive cigarette purchasers, CVS+ (CVS and other retailers), and other-exclusive (only non-CVS retailers). In state-level analyses using retailers' point-of-sale purchase data, an interrupted time series compared cigarette purchasing before (January 2012 to August 2014) and after (September 2014 to April 2015) tobacco removal in 13 intervention states with CVS market share of at least 15% versus 3 control states with no CVS stores. RESULTS: Compared with other-exclusive purchasers, CVS-exclusive purchasers were 38% likelier (95% confidence interval = 1.06, 1.81) to stop cigarette purchasing after tobacco removal. Compared with control states, intervention states had a significant mean decrease of 0.14 (95% confidence interval = 0.06, 0.22) in packs per smoker per month. CONCLUSIONS: After CVS's tobacco removal, household- and population-level cigarette purchasing declined significantly. Private retailers can play a meaningful role in restricting access to tobacco. This highlights one approach to reducing tobacco use and improving public health.

Longitudinal Patterns of Spending Enhance the Ability to Predict Costly Patients: A Novel Approach to Identify Patients for Cost Containment.

BACKGROUND: With rising health spending, predicting costs is essential to identify patients for interventions. Many of the existing approaches have moderate predictive ability, which may result, in part, from not considering potentially meaningful changes in spending over time. Group-based trajectory modeling could be used to classify patients into dynamic long-term spending patterns. OBJECTIVES: To classify patients by their spending patterns over a 1-year period and to assess the ability of models to predict patients in the highest spending trajectory and the top 5% of annual spending using prior-year predictors. SUBJECTS: We identified all fully insured adult members enrolled in a large US nationwide insurer and used medical and prescription data from 2009 to 2011. RESEARCH DESIGN: Group-based trajectory modeling was used to classify patients by their spending patterns over a 1-year period. We assessed the predictive ability of models that categorized patients in the top fifth percentile of annual spending and in the highest spending trajectory, using logistic regression and split-sample validation. Models were estimated using investigator-specified variables and a proprietary risk-adjustment method. RESULTS: Among 998,651 patients, in the best-performing model, prediction was strong for patients in the highest trajectory group (C-statistic: 0.86; R: 0.47). The C-statistic of being in the top fifth percentile of spending in the best-performing model was 0.82 (R: 0.26). Approaches using nonproprietary investigator-specified methods performed almost as well as other risk-adjustment methods (C-statistic: 0.81 vs. 0.82). CONCLUSIONS: Trajectory modeling may be a useful way to predict costly patients that could be implementable by payers to improve cost-containment efforts.

An Insurer's Care Transition Program Emphasizes Medication Reconciliation, Reduces Readmissions And Costs.

Adverse drug events and the challenges of clarifying and adhering to complex medication regimens are central drivers of hospital readmissions. Medication reconciliation programs can reduce the incidence of adverse drug events after discharge, but evidence regarding the impact of medication reconciliation on readmission rates and health care costs is less clear. We studied an insurer-initiated care transition program based on medication reconciliation delivered by pharmacists via home visits and telephone and explored its effects on high-risk patients. We examined whether voluntary program participation was associated with improved medication use, reduced readmissions, and savings net of program costs. Program participants had a 50 percent reduced relative risk of readmission within thirty days of discharge and an absolute risk reduction of 11.1 percent. The program saved $2 for every $1 spent. These results represent real-world evidence that insurer-initiated, pharmacist-led care transition programs, focused on but not limited to medication reconciliation, have the potential to both improve clinical outcomes and reduce total costs of care.