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.

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.

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.

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.

Persistent opioid use following cesarean delivery: patterns and predictors among opioid-naïve women.

BACKGROUND: The incidence of opioid-related death in women has increased 5-fold over the past decade. For many women, their initial opioid exposure will occur in the setting of routine medical care. Approximately 1 in 3 deliveries in the United States is by cesarean, and opioids are commonly prescribed for postsurgical pain management. OBJECTIVE: The objective of this study was to determine the risk that opioid-naïve women prescribed opioids after cesarean delivery will subsequently become consistent prescription opioid users in the year following delivery and to identify predictors for this behavior. STUDY DESIGN: We identified women in a database of commercial insurance beneficiaries who underwent cesarean delivery and who were opioid naïve in the year prior to delivery. To identify persistent users of opioids, we used trajectory models, which group together patients with similar patterns of medication filling during follow-up, based on patterns of opioid dispensing in the year following cesarean delivery. We then constructed a multivariable logistic regression model to identify independent risk factors for membership in the persistent user group. RESULTS: A total of 285 of 80,127 (0.36%, 95% confidence interval, 0.32-0.40), opioid-naïve women became persistent opioid users (identified using trajectory models based on monthly patterns of opioid dispensing) following cesarean delivery. Demographics and baseline comorbidity predicted such use with moderate discrimination (c statistic = 0.73). Significant predictors included a history of cocaine abuse (risk, 7.41%; adjusted odds ratio, 6.11, 95% confidence interval, 1.03-36.31) and other illicit substance abuse (2.36%; adjusted odds ratio, 2.78, 95% confidence interval, 1.12-6.91), tobacco use (1.45%; adjusted odds ratio, 3.04, 95% confidence interval, 2.03-4.55), back pain (0.69%; adjusted odds ratio, 1.74, 95% confidence interval, 1.33-2.29), migraines (0.91%; adjusted odds ratio, 2.14, 95% confidence interval, 1.58-2.90), antidepressant use (1.34%; adjusted odds ratio, 3.19, 95% confidence interval, 2.41-4.23), and benzodiazepine use (1.99%; adjusted odds ratio, 3.72, 95% confidence interval, 2.64-5.26) in the year prior to the cesarean delivery. CONCLUSION: A very small proportion of opioid-naïve women (approximately 1 in 300) become persistent prescription opioid users following cesarean delivery. Preexisting psychiatric comorbidity, certain pain conditions, and substance use/abuse conditions identifiable at the time of initial opioid prescribing were predictors of persistent use.

The adherence impact of a program offering specialty pharmacy services to patients using retail pharmacies.

BACKGROUND: A new service model integrates the specialty pharmacy's comprehensive service with the retail pharmacy's patient contact, giving patients options for medication delivery to home, pharmacy, or doctor's office. OBJECTIVE: Evaluate the impact of the new service model on medication adherence. DESIGN: Retrospective cohort study SETTINGS: One hundred fifteen CVS retail stores in Philadelphia participated in a pilot from May 2012 to October 2013, and 115 matched CVS retail stores from around the nation served as controls. PATIENTS: All eligible patients from the intervention and control stores received specialty medications through CVS retail pharmacies prior to implementation of the new service model. INTERVENTION: The intervention patients were transitioned from retail pharmacy service to the specialty pharmacy with delivery options. The control patients received standard retail pharmacy services. MAIN OUTCOME MEASURES: Proportion of days covered and first fill persistence were tracked for 12 months before and after program implementation. RESULTS: Under the new service model, 228 patients new to therapy in the post period had a 17.5% increase in the rate of obtaining a second fill as compared to matched controls. Patients on therapy in both the pre- and the post-periods had a pre-post increase of 6.6% in average adherence rates and a pre-post increase of 10.8% in optimal adherence rates as compared to 326 matched controls. CONCLUSION: The study demonstrated significant improvement in both adherence to therapy and first-fill persistence among patients in the new service model integrating specialty pharmacy's comprehensive services with the retail pharmacy's patient contact and medication delivery choices.

Can purchasing information be used to predict adherence to cardiovascular medications? An analysis of linked retail pharmacy and insurance claims data.

OBJECTIVE: The use of retail purchasing data may improve adherence prediction over approaches using healthcare insurance claims alone. DESIGN: Retrospective. SETTING AND PARTICIPANTS: A cohort of patients who received prescription medication benefits through CVS Caremark, used a CVS Pharmacy ExtraCare Health Care (ECHC) loyalty card, and initiated a statin medication in 2011. OUTCOME: We evaluated associations between retail purchasing patterns and optimal adherence to statins in the 12 subsequent months. RESULTS: Among 11 010 statin initiators, 43% were optimally adherent at 12 months of follow-up. Greater numbers of store visits per month and dollar amount per visit were positively associated with optimal adherence, as was making a purchase on the same day as filling a prescription (p<0.0001 for all). Models to predict adherence using retail purchase variables had low discriminative ability (C-statistic: 0.563), while models with both clinical and retail purchase variables achieved a C-statistic of 0.617. CONCLUSIONS: While the use of retail purchases may improve the discriminative ability of claims-based approaches, these data alone appear inadequate for adherence prediction, even with the addition of more complex analytical approaches. Nevertheless, associations between retail purchasing behaviours and adherence could inform the development of quality improvement interventions.

Patients' Satisfaction with and Preference for Telehealth Visits.

BACKGROUND: One-quarter of U.S. patients do not have a primary care provider or do not have complete access to one. Work and personal responsibilities also compete with finding convenient, accessible care. Telehealth services facilitate patients' access to care, but whether patients are satisfied with telehealth is unclear. OBJECTIVE: We assessed patients' satisfaction with and preference for telehealth visits in a telehealth program at CVS MinuteClinics. DESIGN: Cross-sectional patient satisfaction survey. PARTICIPANTS: Patients were aged ≥18 years, presented at a MinuteClinic offering telehealth in January-September 2014, had symptoms suitable for telehealth consultation, and agreed to a telehealth visit when the on-site practitioner was busy. MAIN MEASURES: Patients reported their age, gender, and whether they had health insurance and/or a primary care provider. Patients rated their satisfaction with seeing diagnostic images, hearing and seeing the remote practitioner, the assisting on-site nurse's capability, quality of care, convenience, and overall understanding. Patients ranked telehealth visits compared to traditional ones: better (defined as preferring telehealth), just as good (defined as liking telehealth), or worse. Predictors of preferring or liking telehealth were assessed via multivariate logistic regression. KEY RESULTS: In total, 1734 (54 %) of 3303 patients completed the survey: 70 % were women, and 41 % had no usual place of care. Between 94 and 99 % reported being "very satisfied" with all telehealth attributes. One-third preferred a telehealth visit to a traditional in-person visit. An additional 57 % liked telehealth. Lack of medical insurance increased the odds of preferring telehealth (OR = 0.83, 95 % CI, 0.72-0.97). Predictors of liking telehealth were female gender (OR = 1.68, 1.04-2.72) and being very satisfied with their overall understanding of telehealth (OR = 2.76, 1.84-4.15), quality of care received (OR = 2.34, 1.42-3.87), and telehealth's convenience (OR = 2.87, 1.09-7.94) CONCLUSIONS: Patients reported high satisfaction with their telehealth experience. Convenience and perceived quality of care were important to patients, suggesting that telehealth may facilitate access to care.

Observing versus Predicting: Initial Patterns of Filling Predict Long-Term Adherence More Accurately Than High-Dimensional Modeling Techniques.

OBJECTIVE: Despite the proliferation of databases with increasingly rich patient data, prediction of medication adherence remains poor. We proposed and evaluated approaches for improved adherence prediction. DATA SOURCES: We identified Medicare beneficiaries who received prescription drug coverage through CVS Caremark and initiated a statin. STUDY DESIGN: A total of 643 variables were identified at baseline from prior claims and linked Census data. In addition, we identified three postbaseline predictors, indicators of adherence to statins during each of the first 3 months of follow-up. We estimated 10 models predicting subsequent adherence, using logistic regression and boosted logistic regression, a nonparametric data-mining technique. Models were also estimated within strata defined by the index days supply. RESULTS: In 77,703 statin initiators, prediction using baseline variables only was poor with maximum cross-validated C-statistics of 0.606 and 0.577 among patients with index supply ≤30 days and >30 days, respectively. Using only indicators of initial statin adherence improved prediction accuracy substantially among patients with shorter initial dispensings (C = 0.827/0.518), and, when combined with investigator-specified variables, prediction accuracy was further improved (C = 0.842/0.596). CONCLUSIONS: Observed adherence immediately after initiation predicted future adherence for patients whose initial dispensings were relatively short.

Patient Use of Email, Facebook, and Physician Websites to Communicate with Physicians: A National Online Survey of Retail Pharmacy Users.

BACKGROUND: Patient-physician communication often occurs outside the clinic setting; many institutions discourage electronic communication outside of established electronic health record systems. Little empirical data are available on patient interest in electronic communication and Web-based health tools that are technically feasible but not widely available. RESEARCH OBJECTIVE: To explore patient behavior and interest in using the Internet to contact physicians. DESIGN: National cross-sectional online survey. PARTICIPANTS: A sample of 4,510 CVS customers with at least one chronic condition in the household was used to target patients with chronic conditions and their caregivers. Subjects were identified from a national panel of over 100,000 retail pharmacy customers. Of those sampled, 2,252 responded (50.0 % response rate). MAIN MEASURES: Survey measures included demographic and health information, patient use of email and Facebook to contact physicians, and patient interest in and use of Web-based tools for health. KEY RESULTS: A total of 37 % of patients reported contacting their physicians via email within the last six months, and 18 % via Facebook. Older age was negatively associated with contacting physicians using email (OR 0.57 [95 % CI 0.41-0.78]) or Facebook (OR 0.28 [0.17-0.45]). Non-white race (OR 1.61 [1.18-2.18] and OR 1.82 [1.24-2.67]) and caregiver status (OR 1.58 [1.27-1.96] and OR 1.71 [1.31- 2.23]) were positively associated with using email and Facebook, respectively. Patients were interested in using Web-based tools to fill prescriptions, track their own health, and access health information (37-57 %), but few were currently doing so (4-8 %). CONCLUSIONS: In this population of retail pharmacy users, there is strong interest among patients in the use of email and Facebook to communicate with their physicians. The findings highlight the gap between patient interest for online communication and what physicians may currently provide. Improving and accelerating the adoption of secure Web messaging systems is a possible solution that addresses both institutional concerns and patient demand.

Economic value of pharmacist-led medication reconciliation for reducing medication errors after hospital discharge.

OBJECTIVES: Medication discrepancies at the time of hospital discharge are common and can harm patients. Medication reconciliation by pharmacists has been shown to prevent such discrepancies and the adverse drug events (ADEs) that can result from them. Our objective was to estimate the economic value of nontargeted and targeted medication reconciliation conducted by pharmacists and pharmacy technicians at hospital discharge versus usual care. STUDY DESIGN: Discrete-event simulation model. METHODS: We developed a discrete-event simulation model to prospectively model the incidence of drug-related events from a hospital payer's perspective. The model assumptions were based on data published in the peer-reviewed literature. Incidences of medication discrepancies, preventable ADEs, emergency department visits, rehospitalizations, costs, and net benefit were estimated. RESULTS: The expected total cost of preventable ADEs was estimated to be $472 (95% credible interval [CI], $247-$778) per patient with usual care. Under the base-case assumption that medication reconciliation could reduce medication discrepancies by 52%, the cost of preventable ADEs could be reduced to $266 (95% CI, $150-$423), resulting in a net benefit of $206 (95% CI, $73-$373) per patient, after accounting for intervention costs. A medication reconciliation intervention that reduces medication discrepancies by at least 10% could cover the initial cost of intervention. Targeting medication reconciliation to high-risk individuals would achieve a higher net benefit than a nontargeted intervention only if the sensitivity and specificity of a screening tool were at least 90% and 70%, respectively. CONCLUSIONS: Our study suggests that implementing a pharmacist-led medication reconciliation intervention at hospital discharge could be cost saving compared with usual care.