Pharmaceutical manufacturers, like all healthcare players, are constantly looking for tools that help them make better business decisions, control costs, improve bottom lines, and contribute to better health. Most believe that longitudinal patient care information is among the most promising tools, capable of providing answers that can lead to success.
The trouble is that true longitudinal patient records remain an ideal: records that capture clinical, financial, and administrative information across all sites of care – physician’s office, hospital, clinic, nursing home, laboratory – throughout the patient’s lifetime. Instead, we rely today on a variety of available data sources, enhance them with analytics, and integrate them to simulate such lifetime patient records. We access pharmacy dispensing and medical claims records, electronic medical records, clinical trial data, and government data in this quest.
Each of these data types has advantages and disadvantages. For example, pharmacy terminal data may be useful for monitoring dispensed prescriptions or identifying/quantifying switching patterns, but inappropriate for determining the diagnosis underlying a prescription or the presence of comorbid conditions (e.g., patients with hypertension and elevated lipid values) that might reveal additional treatment opportunity.
Controlled clinical trial data provide a window on outcomes and adverse events, but small sample sizes, restrictive inclusion/exclusion criteria, and cost limit its use as a surrogate for real-world, post-launch treatment practices. Government data are inexpensive and encompassing all sites of care, but Medicare data lacks drug benefits and Medicaid’s everchanging enrollment makes longitudinal patient analysis challenging. Medical and pharmacy claims data are ideal for analyzing resource utilization and cost of illness, but are limited in clinical detail (e.g., lab test charges, but no results).
Finally, electronic medical records offer rich clinical detail (e.g., lab test results, physical exam results, diagnosis and prescribed therapy) but not the related financial and administrative data. Moving to the next stage in the evolution of longitudinal patient information will require marrying clinical depth to financial information across treatment settings.
Though not yet reaching the ideal, vast amounts of longitudinal patient data are available today. For example, IMS Health offers a variety of databases including Lifelink[TM]: Medical Records Solutions that cover more than 700,000 patients of primary care physicians by accessing office-based electronic medical records anonymously. The data are encrypted on site, with neither physician nor patient identified.
How is longitudinal patient information being used today, and by whom? Healthcare managers and providers use these data to identify best practices that can result in better outcomes. The data help reduce or contain costs by contributing to disease management programs and by qualifying risks. Pharmaceutical manufacturers need to demonstrate product-value to influence the behavior of their customers and their customer’s customer, the patient. Eventually, patients too, will use longitudinal information to select providers based on outcome profiles developed for their particular health problems.
The bottom line is that all stakeholders want to make better decisions – and pharmaceutical manufacturers who “touch” virtually all other healthcare stakeholders may have the most to gain from longitudinal patient data analysis. The benefits were exhibited in the results of a survey conducted by Coopers & Lybrand in 1996. The respondents were approximately 60 vice presidents and directors that are responsible for various functions within pharmaceutical companies.
* Market research said the most important uses of available longitudinal patient data were to uncover unmet needs, assess market potential, demonstrate product value, support sales and marketing efforts, and build managed care relationships – quite a treasure of issues to be addressed.
* Outcomes research expressed strong interest in building disease models, thus enhancing protocol designs, and developing disease management programs.
* Product development placed assessing market potential at the top of the list, but also mentioned understanding and influencing patient behavior, identifying unmet needs, and competitive product positioning.
In short, patient-level data provide key input for making a wide variety of business decisions. To demonstrate how the data strengthen decision making and provide significant competitive advantage, here are three case histories from the perspective of a pharmaceutical manufacturer.
Lost time is lost sales and fewer patient benefits
Your objective is to capture the largest share of high-risk patients in the cholesterol market. These patients are most likely to be treated with a drug immediately following diagnosis, rather than being started on a non-drug therapy. You have specified “high risk” as hyperlipidemia/hypercholesterolemia patients with one or more of the following characteristics:
From this subset, which patients are most likely to receive drug therapy on the day they are diagnosed? Electronic medical record data provide the clinical depth ([ILLUSTRATION FOR FIGURE 2 OMITTED], below). First, of the patients who were prescribed drug therapy within one year of diagnosis, 40 percent received a drug on the day of diagnosis. Second, patients with ischemic heart disease and/or diabetes, and/or HDL[less than] 35 are most likely to be prescribed drug therapy immediately. These are the patients you want to target.
Three months after diagnosis, close to 70 percent of these patients had received a prescription. The time gap for 30 percent of the patients who will be prescribed drug therapy within the first year represents potential prescriptions lost by delay – sales that can never be recaptured.
Re-forecasting sales potential
You are interested in quantifying the demand for a product to treat an AIDS-related opportunistic infection. New antiretrovital treatment regimens, including protease inhibitors, seem to have a positive impact on disease progression such that fewer patients contract the opportunistic infection for which your product is indicated. If this is the case, sales forecasts should reflect this change.
Longitudinal electronic medical record data evaluates the change over time of opportunistic infection rates after the introduction of new AIDS treatment regimens. The data reveal that regardless of the infection, rates decreased with the use of the new treatment options ([ILLUSTRATION FOR FIGURE 3 OMITTED], above).
Incidence of Wasting, for example, was five percent for patients taking anti-retroviral regimens that included protease inhibitors vs. nine percent for patients taking anti-retrovirals alone. Similar decreases in the incidents of MAC, Kaposi’s sarcoma, and CMV retinitis were seen for patients who added the new protease inhibitor options to their treatment regimens.
With this information, sales can be reforecast to reflect the changed total market and the relative market shares. Clearly, the clinical richness of the data adds a new level of accuracy in assessing market potential and setting realistic sales expectations.
Combination therapy cuts total cost
You want to quantify the economic value of a new combination regimen for peptic ulcers. Currently, there is one generally accepted treatment for the condition. Combination therapy will no doubt increase drug costs, but will this treatment approach lower hospitalization rates and thus lower total costs?
A retrospective outcomes study using claims data should be able to quantify hospitalization rates and total cost experience for three treatment options: current, new, and combined therapies. This database approach provides the information across various different sites of care.
Results show that current treatment has a 12-month hospitalization rate of 6.2 percent. New drug treatment reduces the hospitalization rate to 4.6 percent and combination therapy has the lowest rate at 4.3 percent. Reflecting hospitalization charges, total cost-per-patient was highest with current treatment and lowest for the combination therapy.
Thus the conclusion – higher drug costs do not necessarily imply higher total patient costs. This insight becomes invaluable when marketing to formulary decision-makers and other managed care executives. It helps foster the trend to review total patient costs rather than the cost of each segment of the therapy.
Each of these studies demonstrates the rich intelligence obtained from using longitudinal patient data now available from a variety of sources, including electronic patient record access. While each scenario resulted in different insights and actions, they all required a look at patient populations and their care over time – and they all improved decision making.
Today’s healthcare data sources can significantly improve the accuracy and effectiveness of business solutions while we all wait for the perfect longitudinal patient record.