With the increase in the number of and budgetary expense for specialty pharmaceuticals, there is a critical need to analyze and associate treatment outcomes related to those drugs. According to "Medicine Use and Spending in the US," published by the IQVIA Institute, specialty drugs compose just 1.9% of total prescription volume but account for 37.4% of spending within the retail and mail-order distribution channels. In the non-retail setting, specialty drugs account for 60% of invoice spending and 2.3% of standard unit volumes. This trend highlights the need to show that the cost is justified by superior outcomes. This applies to traditional fee for service reimbursement but even more so for at risk, value based contracts which are growing in number every year.
Because health systems are now assuming financial risk for the outcomes of certain patient populations, they are vulnerable if mandated outsourcing for specialty pharmaceuticals occurs. This could happen because they are not granted access to a limited distribution drug (LDD) by the manufacturer or because the payer has not included the health system in their network. Either scenario causes the health system to lose control of direct care for a patient that they could be financially responsible.
Outcome Measurement Parameters
Health systems often have robust data collected within their Electronic Health Record (EHR) that could help to determine comparative analytics for various patient cohorts. A key problem is that much of the most critical data is in unstructured, non-discrete forms and formats that are hard to search and even harder to analyze without advanced technologies.
Acentrus Specialty, with the help of OM1, Inc., took on the challenging task of trying to study this data to determine various end points. An initial attempt was started in January 2017 to study real world evidence (RWE) with a goal of true risk-adjusted outcome comparisons. As a starting point, three disease states were chosen to evaluate and seven health systems volunteered to participate in the analysis. The disease states were multiple sclerosis (MS), rheumatoid arthritis (RA), and malignant melanoma (MM). The project included certain comparator endpoints that would determine equivalence or superiority between cohorts. Those endpoints included: 1) comparative risk adjusted outcome performance between health systems within the disease states; 2) risk adjusted outcome performance between centers with access to LDD and payer networks where the patient care was totally within the health system vs. centers that were required to outsource their patients to a third party for care; and 3) measures of current performance indicators like turnaround time (TAT), abandonment rate, portion days covered (PDC), and other metrics that have some statistical correlation to patient outcomes at the patient level for each disease.
Outcome Project Timeline
The project began around the beginning of 2017 with the creation of 19 custom SQL scripts to extract specific structured and unstructured fields from each sites EHR reporting database. The SQL queries required slight customization for each health system as each EHR instance was slightly different. Acentrus resources had to work closely with health system IT resources to generate the output needed for the project. For complete data collection for all 7 sites, it took close to a year. Setting up the specialized analytic system to process both structured and unstructured data for these disease states for ongoing measurement took another 3 months, so this was a lengthy project. The good news is that the 19 SQL views can be used for other disease-specific studies in the future and the framework for comparative outcome analysis is complete.
Data were received by OM1 and processed in accordance with an emerging standard framework for outcomes evaluation that has been developed by OM1 under contract to the Department of Health and Human Services. This approach uses a highly sophisticated approach to risk adjustment of baseline characteristics and a common, multi-dimensional model for evaluating patient-level outcomes that are relevant to the clinician and patient. As a further challenge, the approach requires use of both structured and unstructured data to achieve sufficient clinically relevant data points, as many of these exist only in the clinician notes. For example, in rheumatoid arthritis, the starting point for disease activity is a critical factor for understanding how a patient responds to treatment. Similarly, for MS, the starting and ending points of accumulated disability as well as events such as relapses are critical to benchmark institutions with respect to their patient populations as well as to understand their outcomes over time. In addition, given the fact that these patients may have multiple, significant comorbidities, a much more granular approach to comorbid risk adjustment is needed to make valid comparisons between centers and treatment paradigms. For this reason, the OM1 Medical Burden Index, or OMBI ™, was used in addition to other comorbidity measures. The OMBI leverages over 10,000 variables in the medical record in assessing comorbidities and is highly predictive of future resource utilization with an AUC=0.91. The importance of accurate, clinically based risk-adjustment in the results presented cannot be overstated.
Importance to Patients and Health Systems
This type of Real World Evidence based retrospective analysis identifies best practices and is critical for successful value based care. The Acentrus Specialty Network of health systems is identifying and sharing best practices and superior patient outcome methodologies collectively with other clients in the network. Clients benefit from aggregate benchmarking and patients ultimately get better care. This type of analytics identifies opportunities for improvement as well as areas of superior care. The methodologies that produce superior outcomes become the baseline for the whole network. We are nascent in the journey but EHR data is now being mined for population health and risk based contracting initiatives that will help all stakeholders.
Although this type of data analytics within a network is relatively new and unique in the market, it will likely become the standard if it improves outcomes. The ability to use the deep data collected within an EHR is paramount to how specialty pharmacy and value based care will be delivered in the near future. Acentrus Specialty and OM1 are excited to be at the forefront of this revolution and look forward to adding more health systems and new disease states in the coming months.