Benefits of Prior Authorizations.

DISCLOSURES: No funding supported the writing of this article. The author has nothing to disclose.

Augmenting Cost-Effectiveness Analysis for Uncertainty: The Implications for Value Assessment-Rationale and Empirical Support.

DISCLOSURES: This study received unrestricted funding from the Pharmaceutical Research Manufacturers of America. The authors also do consulting, personally or through their employment, with numerous pharmaceutical manufacturers, payers, and other stakeholders with a general interest in this subject matter.

General practitioners' concepts on issuing out-of-pocket prescriptions for hypnotics and sedatives in Germany.

BACKGROUND: In Germany, almost 50% of prescriptions for benzodiazepines and drugs as Zolpidem and Zopiclone are as out-of-pocket (OOP) prescriptions-requiring patients to buy the drug at their own expense-although almost 90% of the population has statutory health insurance covering medication costs. OBJECTIVE: To understand why general practitioners (GPs) choose this prescribing method since needed medications are insurance covered, and unnecessary drugs should not be prescribed at all. METHODS: In this qualitative study, 17 semi-structured interviews with GPs were conducted, audio recorded and transcribed verbatim. Transcripts were analysed with grounded theory to extract a model explaining the described behaviour. RESULTS: Knowing the significant medical risks and insecurity about regulations makes GPs wish to avoid hypnotics and sedatives. They achieve this by 'Creating a barrier' (central phenomenon) and employing the strategy 'Using an out-of-pocket prescription', which not only generates costs for the patient but also reduces the physicians´ legal and financial accountability. The perceived patient type, expected problem duration and diagnosis influence the decision about the prescription form: patients with an alcohol or drug addiction or those with 'uncomplicated' insomnia are more likely to receive an OOP prescription. Patients with any psychiatric diagnosis will likely receive a statutory health insurance prescription. DISCUSSION: Current regulations do not provide guidance to GPs regarding hypnotics and sedatives. A clear regulatory framework and guidelines could possibly reduce physicians' defensive attitudes about these drugs and their use of OOP prescriptions. The approach to use OOP prescriptions as a barrier to reduce patients' medication use lacks evidence regarding effectiveness.

Expanding the breadth of Medicare: learning from Australia.

The design of Australia's Medicare programme was based on the Canadian scheme, adapted somewhat to take account of differences in the constitutional division of powers in the two countries and differences in history. The key elements are very similar: access to hospital services without charge being the core similarity, universal coverage for necessary medical services, albeit with a variable co-payment in Australia, the other. But there are significant differences between the two countries in health programmes - whether or not they are labelled as 'Medicare'. This paper discusses four areas where Canada could potentially learn from Australia in a positive way. First, Australia has had a national Pharmaceutical Benefits Scheme for almost 70 years. Second, there have been hesitant extensions to Australia's Medicare to address the increasing prevalence of people with chronic conditions - extensions which include some payments for allied health professionals, 'care coordination' payments, and exploration of 'health care homes'. Third, Australia has a much more extensive system of support for older people to live in their homes or to move into supported residential care. Fourth, Australia has gone further in driving efficiency in the hospital sector than has Canada. Finally, the paper examines aspects of the Australian health care system that Canada should avoid, including the very high level of out-of-pocket costs, and the role of private acute inpatient provision.

Comparison of Drug Benefits Provided by Veterans Affairs Canada and the Canadian Forces Health Services Group.

BACKGROUND: Drug benefits are provided at public expense to all actively serving Canadian Armed Forces (CAF) personnel, with ongoing drug coverage offered by Veterans Affairs Canada (VAC) for selected conditions following termination of employment. Differences in drug coverage between these programs could introduce risks for treatment disruption. OBJECTIVES: Work was undertaken to establish a process that would allow systematic comparison of the entire VAC and CAF formularies, and to identify and explain discordant listings in 14 therapeutic categories that pose risk of adverse outcomes with sudden treatment interruption. METHODS: Lists of medications were created for each program, including regular benefit and restricted use drugs, using files obtained from the claims processor in January 2015. Products were coded using the Anatomic-Therapeutic-Chemical (ATC) system. Degree of alignment within therapeutic categories was assessed based on the percentage of fifth-level ATCs that were covered in common. Discordantly listed drugs in 14 categories of concern were reviewed to identify similarities in product characteristics. RESULTS: A total of 1124 medications were identified in 80 therapeutic categories. Coverage of medications was identical in 11 categories, and overall, almost three-quarters of identified drugs (73.4%, n = 825) were covered in common by both plans. Many discordant listings reflected known differences in the programs' operating procedures. A number of discrepancies were also identified in newer therapeutic categories. CONCLUSIONS: There is significant overlap in the medications covered by the CAF and VAC drug benefit programs. Application of the ATC coding system allowed for discrepancies to be readily identified across the entire formulary, and in specific therapeutic categories of concern.

Seguridad del paciente: contribución de un farmacéutico a un equipo multidisciplinar en la atención al paciente de Medicina Interna / Patient safety: the pharmacist's contribution to a multidisciplinary Internal Medicine patient-care team

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Is the Currently Used Prescription Adjudication Date a Good Proxy for Calculating Medication Refill Adherence?

BACKGROUND: The Centers for Medicare & Medicaid Services (CMS) adopted the proportion of days covered (PDC) calculation for use in their Five-Star Quality Rating System for Medicare Advantage and Prescription Drug Plans. This calculation uses the prescription adjudication date (i.e., date the prescription is billed to the benefits manager by a pharmacy) as a proxy for medication adherence. Adherence programs, such as automatic refill programs, have become commonplace in community pharmacy and have been identified by industry leaders as interfering with the ability to accurately measure adherence using PDC. OBJECTIVE: To evaluate the prescription pickup date instead of the currently used adjudication date to calculate PDC in the presence of a community pharmacy automatic refill program. METHODS: This study used a post-only quasi-experimental design with patients aged 65 years or older enrolled in automatic and manual refill programs in a 29-store community pharmacy chain during 2014. PDC was calculated using the prescription adjudication date and pickup date (i.e., date the patient brought the medication home) using pharmacy dispensing data for CMS adherence metrics medications, including statins, renin angiotensin aldosterone system antagonists (RASA), and noninsulin diabetes medications. Mann-Whitney U and effect size calculations evaluated differences in PDC between automatic and manual refill prescriptions for the adjudication date and pickup date, as well as the difference in PDC between adjudication and pickup date. RESULTS: 10,936 prescriptions were included with 21.9% enrolled in the automatic refill program. Mean (SD) adherence was 88.6 (17.6) and 86.4 (17.1) for automatic refills and 85.8 (19.0) and 85.0 (18.9) for manual refills, using the adjudication date and pickup date PDC, respectively. Significant difference existed between automatic and manual refill prescriptions using the adjudication date (P < 0.001) but not for the pickup date. The difference between adjudication and pickup date PDC ranged from 0% to 32.0% for automatic refills and 0% to 38.7% for manual refills. The difference between adjudication and pickup date PDC was significant when comparing automatic and manual refill prescriptions (P < 0.001). CONCLUSIONS: The artificial inflation seen with adjudication date PDC indicates that the prescription pickup date is a more accurate reflection of patient medication taking. Automatic refills resulted in a less reliable PDC compared with manual refill prescriptions. Discussion about the continued use of the adjudication date to calculate PDC is needed. DISCLOSURES: The project described was supported by the Clinical and Translational Science Award (CTSA) program through the NIH National Center for Advancing Translational Sciences (NCATS), grant UL1TR000427. The content is solely the responsibility of the authors and does not necessarily represent the official views of the NIH. Lester is employed as a pharmacist in the pharmacy chain that participated in this study. The authors report no other relevant conflict of interest. Study concept and design were contributed primarily by Lester, along with Look and Chui. Lester took the lead in data collection, along with Look, and data interpretation, along with Look and Chui. The manuscript was written and revised primarily by Lester, along with Look and Chui.

The impact of formulary drug exclusion policies on patients and healthcare costs.

OBJECTIVES: In an attempt to increase the efficiency of their drug benefit policies, insurers are increasingly excluding drugs from their formularies that they deem to be of low value. Our objective was to identify and review empirical evaluations of drug exclusion policies and examine how they affected patients and healthcare costs. STUDY DESIGN: Literature review. METHODS: We performed a literature search to identify empirical studies that evaluated drug exclusion policies. We reviewed each study to determine how the policy impacted patients (ie, if disease control or frequency, or severity of symptoms, were affected) and if healthcare costs (eg, drug expenditures and costs associated with physician office visits, hospitalizations, laboratory tests) changed. RESULTS: We included 26 studies pertaining to 27 drug exclusion policies. Twenty studies reported the impact of 21 drug exclusion policies on patients: 6 (28.6%) policies were reported to have had a positive impact, 6 (28.6%) to have had a negative impact, and 9 (42.8%) to not have impacted patients. Eighteen studies reported the impact of 19 drug exclusion policies on overall healthcare costs: 14 (73.7%) policies were reported to have reduced costs, 1 (5.3%) to have had a neutral impact on costs, and 4 (21.1%) to have increased costs. CONCLUSIONS: Although there were important exceptions, most studies found that drug exclusion policies reduced costs and did not negatively impact patients.

Accepting Medication Therapy Management Recommendations to Add ACEIs or ARBs in Diabetes Care.

BACKGROUND: National guidelines and initiatives have promoted the use of angiotensin-converting enzyme inhibitors (ACEIs) and angiotensin receptor blockers (ARBs) for patients with diabetes. The University of Arizona Medication Management Center (UA-MMC) is contracted by Medicare health plans, pharmacy benefit managers (PBMs), and multiple commercial health insurance plans to provide medication therapy management (MTM) services for plan members. As part of the MTM program, recommendations have been made for those patients who may benefit from the addition of an ACEI/ARB. Although the intervention benefits and guidelines for using ACEIs/ARBs are clear, real-world evidence is needed to understand and potentially increase uptake of guideline interventions among eligible patients. OBJECTIVES: To (a) identify patient characteristics that predict acceptance of guideline recommendations to add ACEI/ARB medications to diabetic treatment via MTM services and (b) examine how well different case characteristics (i.e., patient age and sex, type and number of recommendation attempts, type of health care plan) predict the odds of adding ACEI/ARB medications to diabetic regimens when recommended through an MTM call center. METHODS: This was a retrospective analysis of secondary data provided by the UA-MMC. The de-identified national data included adult plan members with diabetes who the UA-MMC recommended adding an ACEI/ARB prescription based on 2012 national guidelines. The UA-MMC made recommendations by either patient letters, patient phone calls, physician faxes, or any combination thereof. We conducted a binary logistic regression analysis to assess the impact of case characteristics on the likelihood of accepting recommendations to add ACEI/ARB medications. The outcome variable was recommendation acceptance (yes/no), defined as new prescription claims for an ACEI/ARB within 120 days following the recommendation. Five predictor variables were assessed: (1) patient's age quartile; (2) method of communicating recommendations (letter, phone call, fax, or some combination thereof); (3) whether recommendations were made once or twice on separate dates; (4) patient's sex; and (5) type of health care plan. RESULTS: Recommendations were made for 31,495 members of health plans or PBMs that contracted with the UA-MMC. Patients' ages ranged from 19-90 (Mean =72.01; SD =10.21), with females comprising 56% of the sample. The recommendation to add ACEI/ARB medications was accepted for 14.5% (4,559) of patients. In most cases (73%), recommendations occurred via a letter to patients together with a fax to their providers. The fitted model, containing 3 predictor variables (age quartile, type of contact to communicate the recommendations, and whether recommendation contacts were made twice), was statistically significant, χ(2) (10; N = 31,495) = 112.82 (P < 0.001), indicating that the model was able to distinguish between those who did and did not accept UA-MMC's recommendations to add ACEI/ARB medications. The likelihood of recommendation acceptance decreased as patient age increased compared with patients in the first age quartile (ages 19-67; P ≤ 0.005 at all levels). Compared with sending only a provider fax, patients who received all 3 types of contact (provider fax with patient phone call and letter) were estimated to be 1.34 times more likely (34% increase) to have recommendation acceptance ( P = 0.004; 95% CI = 1.10-1.63). Similarly, patients who received only letters were also 1.32 times more likely (32% increase) than provider faxes alone to result in recommendation acceptance ( P = 0.003; 95% CI = 1.10-1.59). Patients for whom recommendations were made twice were less likely to have recommendation acceptance than for those contacted once, controlling for all other predictor variables in the model ( P < 0.001; OR = 0.77; 95% CI = 0.69-0.86). CONCLUSIONS: Recommendations to add an ACEI/ARB to diabetic regimens are more likely to be accepted for younger patients and those who receive recommendations through all 3 communication types (provider fax combined with patient phone call and letter) or just letters than provider faxes alone. Further research is needed to understand why prescribers are not accepting MTM recommendations.

Medication adherence and healthcare disparities: impact of statin co-payment reduction.

OBJECTIVES: Minority patients have lower rates of cardiovascular medication adherence, which may be amenable to co-payment reductions. Our objective was to evaluate the effect of race on adherence changes following a statin co-payment reduction intervention. STUDY DESIGN: Retrospective analysis. METHODS: The intervention was implemented by a large self-insured employer. Eligible individuals in the intervention cohort (n = 1961) were compared with a control group of employees of other companies without such a policy (n = 37,320). As a proxy for race, we categorized patients into tertiles based on the proportion of black residents living in their zip code of residence. Analyses were performed using difference-in-differences design with generalized estimating equations. RESULTS: Prior to the new co-payment policy, adherence rates were higher for individuals living in areas with fewer black residents. In multivariable models adjusting for demographic factors, clinical covariates and baseline trends, the co-payment reduction increased adherence by 2.0% (P = .14), 2.1% (P = .15) and 6% (P < .0001) for intervention patients living in areas with the bottom, middle and top tertiles of the proportion of black residents. These results persisted after adjusting for income. CONCLUSIONS: Co-payment reduction for statins preferentially improved adherence among patients living in communities with a higher proportion of black residents. Further research is needed on the impact of value-based insurance design programs on reducing racial disparities in cardiovascular care.

Copago en farmacia de receta en la sanidad pública española: certezas, riesgos y selección de riesgos / Co-payment on prescription drugs in the Spanish public health system: Certainty, risk and selection of risks

El modelo de copago en farmacia de receta del SNS cambió el 1 de julio de 2012. Hacía más de 3 décadas que no se modificaba. En este artículo se hace un pequeño recuerdo histórico de la evolución del modelo de copago en farmacia de receta del SNS introducido por primera vez en 1967. Se comparan las características básicas del mismo con el copago en farmacia de receta del Mutualismo Administrativo. Se proporciona información detallada referida al porcentaje de copago efectivo, los efectos recaudatorios, la participación del paciente, entre otros, en ambos modelos. Por último, se señalan las mejoras pendientes no abordadas por la modificación de 2012, como son la concentración del copago en la población de pacientes activos y la selección de riesgos promovida por las diferencias en la aportación de ambos modelos de copago (SNS y Mutualista)

The model of co-payment on prescription drugs in the Spanish National Health System (NHS) changed on 1 July 2012. For more than three decades that it was not modified. This article provides a brief historical reminder of the evolution of this model of co-payment. The basic characteristics of this model are compared with the model of copayment on prescription drugs of the Administrative Mutualism (Civil Servants). The document provides detailed information on the percentage of effective copayment, fundraising effects, the economic participation of the patient, among others, in both models. Finally, listed pending improvements not addressed by 2012 changes such as the concentration of the co-payment in the active patient population and risk selection promoted by the differences in the financial contribution between the two models of co-payment (NHS and Mutualist)

The Australian Pharmaceutical Benefits Scheme data collection: a practical guide for researchers.

BACKGROUND: The Pharmaceutical Benefits Scheme (PBS) is Australia's national drug subsidy program. This paper provides a practical guide to researchers using PBS data to examine prescribed medicine use. FINDINGS: Excerpts of the PBS data collection are available in a variety of formats. We describe the core components of four publicly available extracts (the Australian Statistics on Medicines, PBS statistics online, section 85 extract, under co-payment extract). We also detail common analytical challenges and key issues regarding the interpretation of utilisation using the PBS collection and its various extracts. CONCLUSIONS: Research using routinely collected data is increasing internationally. PBS data are a valuable resource for Australian pharmacoepidemiological and pharmaceutical policy research. A detailed knowledge of the PBS, the nuances of data capture, and the extracts available for research purposes are necessary to ensure robust methodology, interpretation, and translation of study findings into policy and practice.

Análisis modal de fallos y efectos en las prescripciones farmacológicas informatizadas / Failure mode and effects analysis on computerized drug prescriptions

Objetivo. Determinar y analizar los errores en las prescripciones farmacológicas de pacientes asistidos en un hospital de alta resolución mediante la aplicación de un análisis modal de fallos y efectos (AMFE). Material y métodos. Un grupo multidisciplinar de distintas especialidades médicas y de enfermería analizó historias clínicas, en las que las prescripciones farmacológicas se realizaban en formato de texto libre. Se desarrolló un AMFE en el que el índice de prioridad de riesgos (IPR) se obtuvo a partir de un estudio observacional transversal, mediante una auditoría de historias clínicas realizada en 2 fases: 1) verificación previa a la intervención y 2) evaluación de las acciones de mejora después del primer análisis. El tamaño muestral auditable, calculado con técnica de muestreo estratificado y selección aleatoria de episodios clínicos, fue de 679 historias clínicas. Resultados. Se incluyó a 2.096 pacientes. Los errores de prescripción respecto del total de prescripciones descendieron en la segunda fase un 22,2%. En las variables con IPR mayor («vía de administración no especificada» y «dosificación no especificada») no se observaron descensos significativos en la segunda fase, durante la cual no se detectó «pauta horaria incorrecta», «contraindicación del fármaco por alergia», «paciente incorrecto» ni «duplicidad de prescripción», lo que redundó en la mejora de las prescripciones. Conclusiones. Se han determinado y analizado errores de prescripciones farmacológicas mediante la metodología AMFE, mejorando la seguridad clínica de dichas prescripciones. La herramienta permite monitorizar actualizaciones del sistema de prescripción electrónica. Para evitar dichos errores se requeriría que todos los apartados de una prescripción fueran de registro obligado (AU)

Objective. To identify and analyze errors in drug prescriptions of patients treated in a «high resolution» hospital by applying a Failure mode and effects analysis (FMEA).Material and methods A multidisciplinary group of medical specialties and nursing analyzed medical records where drug prescriptions were held in free text format. An FMEA was developed in which the risk priority index (RPI) was obtained from a cross-sectional observational study using an audit of the medical records, carried out in 2 phases: 1) Pre-intervention testing, and (2) evaluation of improvement actions after the first analysis. An audit sample size of 679 medical records from a total of 2,096 patients was calculated using stratified sampling and random selection of clinical events. Results. Prescription errors decreased by 22.2% in the second phase. FMEA showed a greater RPI in «unspecified route of administration» and «dosage unspecified», with no significant decreases observed in the second phase, although it did detect, «incorrect dosing time», «contraindication due to drug allergy», «wrong patient» or «duplicate prescription», which resulted in the improvement of prescriptions. Conclusions. Drug prescription errors have been identified and analyzed by FMEA methodology, improving the clinical safety of these prescriptions. This tool allows updates of electronic prescribing to be monitored. To avoid such errors would require the mandatory completion of all sections of a prescription (AU)

Assessment of the Quality of the Clinical Evidence in Submissions to the Australian Pharmaceutical Benefits Advisory Committee: Fit for Purpose?

BACKGROUND: Assessments of the comparative clinical (and cost) effectiveness of new medicines are increasingly being used to inform decisions on their reimbursement. Assessments of added clinical benefit are invariably based on evidence generated to support registration. OBJECTIVE: Our objective was to identify and characterize significant problems relating to the quality of the clinical evidence in submissions to the Australian Pharmaceutical Benefits Advisory Committee (PBAC) seeking subsidy on the Pharmaceutical Benefits Scheme and thus determine whether the evidence presented to the committee was "fit for purpose." METHODS: We conducted a retrospective analysis of submissions considered by the PBAC between 2005 and 2012 using a published evaluation framework. We developed an additional framework to categorize significant problems in more detail. Significant problems related to the choice of comparator, the unavailability of randomized clinical trial evidence, poor-quality data, a claim of clinical superiority, and a claim of clinical noninferiority. RESULTS: We identified 261 significant problems in 479 major submissions. There was a significant problem with the sponsor's choice of comparator in 11% of the submissions. The most common significant problem (29%) was the determination of a medicine's comparative performance in the target patient population. CONCLUSIONS: The supporting clinical evidence is the foundation of a PBAC submission. We found a poor fit for purpose; on average, one in every two major submissions had a significant problem with the supporting evidence. The findings from our study, if confirmed in other jurisdictions, raise important questions regarding what clinical evidence should be generated to support the reimbursement of new medicines.

Registros electrónicos de prescripción de Atención Primaria: ¿una fuente de información segura? / Are primary care clinics’ electronic prescription databases secure?

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Discrepancies identified with the use of prescription claims and diagnostic billing data following a comprehensive medication review.

BACKGROUND: The University of Florida College of Pharmacy's Medication Therapy Management Communication and Care Center (UF MTMCCC) provides medication therapy management (MTM) services to patients enrolled in a State of Florida Medicaid Waiver Program: Medicaid for the Aged and Disabled. To provide these services, UF MTMCCC was given access to patients' prescription claims data and diagnostic billing data in the form of ICD-9 codes. Prior to calling a patient, a precomprehensive medication review (CMR) work-up was performed to identify potential medication-related problems (MRPs) and/or health-related problems (HRPs). Based on information provided by the patient in relation to comorbidities, medications, and medical history during the interactive telephone conversation, problems were either confirmed or eliminated. All of the reported information was also assessed to identify any new MRPs or HRPs. Accordingly, telephonic MTM services have the potential to bridge the gap between pharmacy claims data and patient self-reported information, since the MTM services provided rely on the accuracy of both informational resources.  OBJECTIVE: To determine the degree of discrepancy in patient-reported information regarding chronic comorbidities and medications versus diagnostic billing data (ICD-9 codes for chronic comorbidities) and pharmacy claims data (medications) when providing MTM services during an interactive telephonic comprehensive medication review.  METHODS: A retrospective chart review (n = 147 patients) was performed for patients who received a telephonic CMR. Pharmacy claims data and diagnostic billing data, in conjunction with the pre-CMR work-up data, were used to identify discrepancies in information obtained from the patient during the CMR. During the chart review, identified MRPs or HRPs were categorized as "confirmed" (patient reported the problem exists and/or it was deduced from the presence/absence of a medication that the problem does exist); "eliminated" (patient reported the problem does not exist and/or it was deduced from the presence/absence of a medication that the problem does not exist); or "new" (a problem that was not identified during precall identification of problems, but following the CMR interaction, it was determined that a problem now exists). The study evaluated the discrepancies before and after a CMR telephonic interaction in the following categories: medications, chronic comorbidities, level 1 drug-drug interactions, level 2 drug-drug interactions, gaps in therapy, therapeutic duplications, lack of therapy, preferred drug list alternatives, combination products, and tobacco use. Percent discrepancy was calculated as the sum of new and eliminated data elements divided by the total number of data elements for each MRP or HRP.  RESULTS: The percent discrepancy observed was 42% for medications, 41% for chronic comorbidities, 77% for level 1 drug-drug interactions, 93% for level 2 drug-drug interactions, 35% for gaps in therapy, 87% for therapeutic duplications, 26% for lack of therapy, 36% for preferred drug list alternatives, 42% for combination products, and 54% discrepancy for report of tobacco use. Overall, 4,441 data elements were identified as confirmed, eliminated, or new across the 147 CMRs. Among those data elements, 56% of the data was confirmed; 23% was eliminated; and 21% was discovered as new.  CONCLUSIONS: The study met its objective in determining the degree of discrepancies that existed when prescription claims data and ICD-9 billing data were used to identify MRPs and/or HRPs versus using patient-reported data. Data revealed that the presence of discrepancy is relatively large depending on the category, indicating a difficulty in accurately making recommendations with incomplete data or solely based on prescription claims and billing data. MTM services with patient interaction are vital in identifying information that allows for more appropriate decision making.