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The United States pharmaceutical market is forecast to grow at a compound annual growth rate of 3.7% from 2013 to 2018. It has been suggested that comparative effectiveness research (CER) may improve the management and use of treatments, leading to more effective health care and decreased spending. Consequently, CER has received much attention recently.
However, although two thirds of people insured in the United States are covered by private health insurance, CER has neglected the viewpoint of private payers. Without the input and participation of commercial payers, comparative effectiveness data may not be optimal for making complex decisions about formulary design, access restrictions, and use controls for the bulk of U.S. patients.
Anonymized patient-level longitudinal data (APLD), which has its roots in claims used by payers, constitutes a common resource for facilitating dialogue between pharmaceutical companies and health plans.
Anonymized patient-level longitudinal data can help in evaluating the effect of health plan features on health care utilization.
Although APLD has been around for some time, it is the linkage of databases of claims with electronic medical records (EMRs) that allows researchers to investigate situations where explanatory variables are not available in either dataset alone. EMR data provide a richer clinical context for interpreting utilization and cost that are observed in claims, and data from claims provide more complete documentation of medical services that may influence the clinical observations in the EMR. The data-linking methods provide the user with strong assurance that linked records represent the same patient.
The linkage of the two datasets is also generally certified for compliance with HIPAA. Therefore, these EMR-linked APLD data are rich and granular, and they provide better context for analysis.
Pharmaceutical companies can develop value propositions for their managed care customers; these customers will understand the strengths and weaknesses of the data and, more importantly, will trust the data. These are the data that managed care decision makers use because it has great value for assessing and comparing both the cost-effectiveness and the clinical effects of competing prescription drugs and other treatment protocols. APLD can provide the basis for defining and extending value propositions beyond traditional safety and efficacy data from clinical trials.
The common belief is that payers prefer randomized controlled clinical trials (RCTs) when comparing drug effectiveness for diseases in which coverage decisions can be implemented. However, payers are interested in study designs beyond RCTs. Payers found prospective nonexperimental studies, such as registries, to be valuable in assessing drug effectiveness and safety. In our experience, most payers value retrospective analyses, such as claims analyses, more highly than RCTs when comparing real-world costs and adherence to drugs.
Payers generally take the view that observational studies are valuable for attaining real-world data with larger study populations to detect rare adverse events, are more likely to include subjects representative of their member populations, and can help them better assess resource use and overall treatment cost.
Retrospective analyses require significant resources because large volumes of data from claims must be extracted and analyzed; nonetheless, they require less work than RCTs. In the authors’ opinion, APLD can support the timely evaluation of the value of new pharmaceutical agents in the marketplace.
APLD captures the full continuum of care in many settings such as physician office visits; hospital stays; retail, mail order, and specialty pharmacies; and carve-out care services. In some cases, linking hospital discharge records with claims data at the patient level has significantly increased the capability of APLD to capture the continuity of a patient’s drug therapy between the inpatient and outpatient settings.
APLD has rich, granular, real-world information that can inform policy and coverage decision making in multiple ways, such as formulary plan design and benefit management support; appropriate utilization and management of new technologies; outcomes and effectiveness studies; population-based modeling; and disease management support.
APLD can support policy and coverage decision making for plan design and formulary management activities. APLD data has plan design information across lives on aspects such as cost sharing, generic incentives/preferred drug options, integrated pharmacy benefits (mail and retail), inclusion/exclusion criteria, prior authorization, and days’ supply limitations.
Also, benefit management information is available for formulary management/policy development, utilization management, drug utilization review (DUR) programs, and target stakeholder benefit management (for the payer, employer, physician, patient, and pharmacist).
There are various applications of APLD in formulary plan design and benefit management, including coverage of a new drug and its tier level as well as control mechanisms such as prior authorization or quantity limits. Because of its longitudinal nature, APLD provides visibility into the cost of switching to a new therapy, provides information about how formulary design affects outcomes, and shows and how formulary incentives drive generics or preferred brands.
Given intertemporal information about patients, one could observe the potential risk of drug interactions, the relationship of polypharmacy to outcomes, and how fulfillment channels (mail/retail) affect utilization.
By analyzing adjudicated longitudinal claims, one can get a better insight (sometimes through inference) into members’ financial contribution relative to drug acquisition cost; impact of generics on brand utilization; appropriateness of concomitant medications; linkage of prescriptions to diagnosis or evidence-based best practices; and economic value in terms of cost-effectiveness and drug cost paid per member per month, as well as tolerability concerns, including discontinuation rates.
Armed with this rich real-world evidence, payers could use new insights for plan design development; evidence-based formulary support; product positioning on formulary; financial risk sharing; education efforts to support utilization; doctor/patient messaging regarding formulary adherence; and value-based purchasing support.
APLD can help evaluate the impact of health plan features on health care utilization and assess the relative performance of plan types with varying managed care features, such as copayments, deductibles, and coverage options in analysis of health care cost and use and measure changes in plan design and benefit characteristics.
Analyses of APLD data have been used for assessing whether to develop a value-based insurance design. For maintenance medications, such as those for congestive heart failure, the effect of copayment on drug adherence was studied by payers as well as pharmaceutical manufacturers because low adherence is known to be associated with poor outcomes.
Barriers to medication affect not only intermediate outcomes (e.g., compliance and/or persistence), but frequent and expensive hard endpoints (e.g., hospitalization, physician visit). To prepare a value-based formulary, the authors analyzed persistent moderate-to-severe asthma patients and the relationship of copayments to hospitalizations. To link this information to accreditation by the National Committee for Quality Assurance (NCQA), the authors defined persistent moderate-to-severe asthma using the NCQA’s Healthcare Effectiveness Data and Information Set (HEDIS).
As copayment levels rose, patients with HEDIS-defined persistent moderate-to-severe asthma had increased risk of respiratory-related hospitalizations.
Appropriate utilization of new technologies includes assessment of the potential effect of advances in science and drug development. It includes assessments for identification of significant breakthrough drugs and other medical interventions/technologies; evaluation of the safety, efficacy, and effectiveness of these interventions; assessments of their potential effect on drug expenditures, e.g., per-member-per-month costs, acquisition costs, cost to treat, cost to switch, and potential medical cost offsets; assessments of their potential effect on clinical, economic, and health outcomes; and development of medical management and appropriate utilization protocols, including prior authorization criteria, step therapies, and treatment protocols.
By analyzing utilization trends and costs as well as benchmarks related to other drugs within or between the classes of interest and modeling the potential impact on the pharmacy budget and estimation of acquisition cost and cost to switch, payers can understand the potential impact of a new therapy in a therapeutic area. They can also learn how existing agents in a class are utilized, going beyond the acquisition cost versus cost to treat within and between agents of interest and any other drug utilization differences.
These types of insights would aid in formulating coverage decisions in advance of launch, positioning of the agent on the formulary, and informing physicians and patients regarding the value of the new agent.
Another application going beyond the burden of illness that APLD offers is to understand how the new intervention will affect health care utilization and tracking of appropriate utilization. The FDA is increasingly requiring use of risk-minimization action plans (RiskMAPs) to define strategy for ensuring appropriate and safe use of newly marketed products.
APLD can play a prominent role in tracking drug use post-launch by providing real-time tracking of dispensed dose/form for drugs with titration concerns; prescriber tracking for educational interventions; patient characterization analyses to quantify longitudinal dose and titration patterns, drug interactions and off-label usage; and surveillance analyses to examine clinical outcomes and/or potential drug-related adverse events.
Research on outcomes and effectiveness seeks to evaluate real-world effectiveness and efficiency of interventions on the health status of patients. It includes an evaluation of a patient population with regard to clinical, economic, and patient-centered outcomes. This analysis extends insights beyond traditional anatomic, physiologic, and pathologic measures obtained from clinical trials and provides an approximation of provider-patient situations.
Analyses of APLD can shed light on many outcomes and effectiveness measures, such as unmet medical needs in the marketplace; burden of illness; incidence and prevalence; physicians’ prescribing patterns and patients’ utilization of drugs; current standards of care compared with national guidelines and best practices; the economic value proposition of a new therapy compared with other treatment options; and any safety and tolerability issues associated with a new technology.
One way to analyze the pattern is to look for the natural history of a disease and its management that includes epidemiologic and resource utilization rates; drug acquisition and cost-to-treat comparisons; cost-of-care and drug cost offset comparisons; cost-effectiveness/cost utility analysis; guideline adherence and quality of care evaluation; compliance/persistence; patient disease identification; and risk stratification algorithms.
APLD helps in understanding the total health cost of a particular medication — as opposed to the direct cost of medication alone. Why? Because a more expensive drug therapy may also produce better outcomes and ultimately reduce long-term medical costs.
One example of real-world use of APLD to guide decision making was a comparison of the current standard of care with national guidelines for statin therapy. When the study was done, clinical guidelines recommended secondary prevention with statin therapy for patients with preexisting cardiovascular disease. The study demonstrated that guideline-based treatment lowers relative mortality risk. APLD analyses are well suited to observational comparative effectiveness research, whether comparing drugs and/or procedures or analyzing the effects of health policy on outcomes, population disparities, or prevention strategies.
Population-based modeling seeks to evaluate the real-world effect on total costs for a population or the budget effect of a health care or pharmacotherapy intervention for a payer’s population.
By analyzing budget impact and cost-effectiveness modeling using clinical trial data on new products, coupled with guidelines on global pharmacoeconomic aspects, payers can understand the real-world effectiveness of a pharmaceutical agent and the time frame associated with “failure/success” treatments, as well as whether the clinical trial results for a new treatment translate into real-world populations. Further, the analyses could reveal the existing pattern(s) of treatment and the best place to insert a new treatment in the continuum and the real “unmet” medical need.
Chronic diseases and medically complex conditions have a significant effect on health care outcomes and medical costs. These include diseases with wide variation in practice standards, diseases with high prevalence rates, and diseases with high associated medical or pharmaceutical costs.
In disease management, APLD insights can reveal diseases that are responsible for high costs and the treatment gap relative to guideline recommendations, the usual treatment approaches, and variability in physician prescribing practices, as well as the morbidity, mortality, and resource utilization rates associated with a given disease. The analyses could also reveal predictors of patient compliance and the broad effects of a disease management program implementation.
Although APLD has been around for some time, its linked claims and EMR data allow researchers to investigate situations where explanatory variables are not available in either dataset alone. EMR data provide a richer clinical context for interpreting utilization and cost observed in claims data, and claims data provide more complete documentation of medical services that may influence the clinical observations in the EMR. And the data-linking methods provide the user with strong assurance that linked records represent the same patient.
APLD enables analysts to conduct a broad range of health services studies, including:
Using APLD for data-driven decision-making support makes sense for payers. Analysis of economics and outcomes from the perspectives of payer, patient, and providers can help improve current formulary plan design and benefit management support, as well as develop new creative solutions.
With costs hard to manage, analysis of APLD will lead to the enhancement of appropriate utilization and the employment of new technologies. APLD will help us understand the effect of decisions on real-world effectiveness, population-based modeling, and disease management support, and can help rationalize and improve health care in the United States.
A blueprint for high-volume, high-quality lung cancer screening that is detecting cancer earlier—and helping to save lives
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