For intravenously administered chemotherapy, standardized prescribing practices, a culture of education with multiple touchpoints, and a controlled environment for dispensing all contribute to increased visibility and patient engagement.1 This stable environment enables care teams to identify potential barriers to medication adherence and maintain more frequent communication with high-risk patients.
This level of visibility and engagement also empowers oncology practices to proactively address avoidable adverse events (AEs), optimizing the overall quality and cost of each episode of care. The influx of oral chemotherapies has created an environment with a lack of standardization, less patient education, and fewer opportunities for patient engagement.
This is fundamentally changing the way oncologists manage episodes of care by shifting responsibilities to the patient and caregiver. The loss of controlled medical supervision can lead to delayed care, a rise in avoidable AEs, and an increased risk of medication noncompliance or abandonment, directly impacting patient outcomes.
The rise in value-based reimbursement models, which require participating practices to track and report on a vast array of new measures related to care quality, cost, and patient outcomes—all while sustaining meaningful cost savings—has required oncologists to assume an unprecedented level of accountability for patients’ well-being across all care settings and episodes of care. Although these programs, such as the Oncology Care Model (OCM), offer financial incentives to participants, practices have been compelled to develop and deploy new capabilities to enable this accountability.
These include the implementation of patient-centered care plans, care navigation services, and technology-enabled solutions to aggregated, disparate clinical and financial data, then transforming these data into meaningful, actionable insights. Although the influx of novel therapies, and particularly oral oncolytics, are heightening the challenges associated with managing quality and cost across episodes of care, these same value-based care tools are enabling oncologists to regain visibility and control.
A Multidisciplinary Team with the Patient at the Center
By establishing a framework for delivering holistic patient care while driving sustainable cost savings, the OCM has become a key reference point for oncology value-based care. Early research into the cost burden of cancer care pointed to hospital admissions and emergency department (ED) visits as 2 of the largest and most impactable opportunities to drive costs savings within cancer care episodes.
It came as no surprise, then, that participants in the OCM program were required to develop patient-centered care plans, including a list of recommended interventions based on patients’ clinical assessments, to help reduce avoidable AEs.
With the recent rise of novel therapies and positive impact of enhanced patient services on reducing ED visits and inpatient hospitalization admissions, drugs’ relative proportion of total costs has increased—now representing more than 50% of the total cost of care—and is only expected to grow. As reimbursement shifts from medical to pharmacy benefit with oral chemotherapies, the resulting co-pay out-of-pocket expenses have also become new, major barriers to access.
This trend necessitates the creation of multidisciplinary teams of care providers to coordinate more diverse interventions across the care continuum. These teams, comprised of nurse navigators, physicians, social workers, at-home care givers, and more, enable a more engaged coordination of care and support the patient’s health objectives as well as their financial and sociodemographic needs.
Although programs may vary across practices, the core elements of an effective, multidisciplinary team-based care program remain the same, including:
Clearly defined and delineated responsibilities for each member of the team, as well as a secure method for cross-departmental communications.
Formation of a robust patient education program, including disease- and drug-specific materials.
Implementation of an effective monitoring program that spans all care settings, enabling the team to track medication adherence and toxicity.
Financial advocacy services in collaboration with payers to help patients better understand their financial responsibility as well as identify assistance programs to reduce barriers to access.
Collaboration with specialty pharmacies to ensure standardization, education and safety checks are appropriately established.
Enhancing Data Management to Track and Optimize Performance
As oncologists face surmounting pressure to find new ways to reduce total costs per episode while ensuring they facilitate delivery of the right treatment to the right patient at the right time, many have begun seeking a deeper understanding of overall drug value—including its impact on AEs, outcomes, and financial toxicity. Historically, oncologists turned to care pathways to inform the most appropriate use of therapies and ascertain the anticipated impact of each therapy on clinical outcomes and costs.
To accommodate new therapy alternatives, care pathways utilization has been on the rise, with 25% of medical oncology clinics using clinical pathways.2 Moving forward, as value-based reimbursement models become more prevalent, long-term success will necessitate the convergence of evidence-based pathways and predictive analytics.
How would this work? By leveraging a comprehensive real-world data set, including input from electronic health records (EHRs), project management systems, claims, genomics reports, lab and pharmacy systems, sociodemographic data and more, oncologists can compare a set of therapies to assess outcomes, then apply statistical models to measure the relative importance of patient-specific variables, from cancer type to diagnosis to lab value—creating a predictive model.
Based on a patient’s specific diagnosis, stage, and other clinical factors, oncologists can leverage predictive models to project the efficacy, toxicity, and total estimated cost for each care pathways option. These insights enable oncologists to make more informed treatment decisions, in alignment with value-based care objectives.
To effectuate a predictive analytics program, practices should continue to invest in more comprehensive approaches to data collection and management. This may include the application of Natural Language Processing (NLP) or Optical Character Recognition (OCR) to quickly and cost-effectively curate data from unstructured fields, such as genomics reports, or partnering with an outside vendor who specializes in data curation and analytics to implement a more comprehensive master data management strategy.
This strategy should include:
Employing a data governance policy that protects patient privacy and ensures compliance with regulatory standards. Whether this includes a HIPAA release to grant access to patients’ health records or informed consent forms for patients involved in research, a robust data collection policy will ensure long-term program success.
Establishing a comprehensive data collection process, including automated data extraction using Extract, Transform and Load (ETL) tools, NLP or OCR, and manual chart abstraction, to ensure all necessary data elements are uncovered.
Aggregating and harmonizing structured and unstructured data. Data must be syntactically scrubbed and semantically normalized by matching data elements across various forms to ensure a consistent nomenclature. The data should then be identity-linked to create de-identified patient cohort “profiles” that longitudinally span the patient journey. This process helps to reduce redundancies and make it consumable by third-party applications for analytics and reporting.
Leveraging a powerful computational engine that can apply algorithms in real time and determine the relative value of each care path option for each unique patient with actionable insights accessible at the point of care. This engine—ideally integrated into the HER—would enable providers to make informed decisions in real time.
Although value-based care has been the primary driving force behind population health analytics and associated data collection initiatives, the rapid growth and high costs of novel drug therapies will fuel a new imperative for data in support of precision medicine.
