Personalized Medicine: Leveraging New Information and Analytics to Support Patient-Centric Care

by Robert Palmer, MBA | President & CEO | PotentiaMED

A significant medical diagnosis is terrifying, and subsequent treatment choices are complex, with profound financial and quality of life implications. There are characteristically several different treatment options, depending on the age of the patient, overall severity of other medical conditions, severity of disease and increasingly genomic information. There are frequently alternative treatments options that are also associated with trade-offs such as survival, quality of life and economic costs. As a result of this multifaceted complexity, and new information physicians are faced with the increasing challenge of recommending treatments and foretelling the future. Patients, the final decision-makers, are frequently left with difficult personal and emotional choices about survival, treatment choices, quality of life, and economic costs.

Patients are different in many ways, yet our current system is largely based on adherence to treatment protocols based on a select group of patients. The existing clinical standards do not adequately address many questions, including the type of patients who should undergo a specific procedure and a given individual’s risk of complications. As a result, estimates suggest that as much as one-third of all health care expenditures generate no clinical benefit.[1] The impact on patients and cost to the system is significant. Consequently, a critical goal in healthcare involves creating strategies to stop treating patients with therapies that generate little, or no, clinical benefit.

Personalization is the future of healthcare. Through new outcomes information and transformational analytical tools, the extraordinarily complex interplay between patient variations, treatment outcomes, and tradeoffs can be captured in ways that support patient-centric care.

Personalized medicine employs advanced analytics, such as statistical models, big data, deep learning (along with other approaches) to rigorously explore the composition of an individual patient’s differentiating genetic, demographic, and ailment attributes (referred to in the sphere as patient-specific factors). The individual patient profile is then used as a starting point for measuring alternative treatments and expected outcomes to define a treatment plan.

Baseline Patient Characteristics

Patient demographic factors, such as sex, age, severity of the disease, racial groups, are important components of a personalized medicine patient profile, and together with other factors, serve as the basis for generating comparison-based outcomes predictions with more relevant data.

Disease-Specific Factors

Disease progression is dynamic, and an individual patient’s stage and secondary disease-specific metrics represent very distinct characteristics, rendering a significant impact on prognosis and treatment decisions. Furthermore, it is important to define the comorbidities that affect a given patient (the presence of other chronic diseases within the patient) and integrate these factors into a patient profile. This element has characteristically remained an alarmingly overlooked sphere of consideration, through both conventional treatment-decision processes and even in early-stage personalized medicine approaches, yet often has profound implications on prognostication calculations and other influential components of the treatment decision process.

New scientific discoveries and technologies are emerging to define important genomic and genetic mutation information, supporting targeted treatments. Genetic polymorphisms and mutations in drug metabolizing enzymes, transporters, receptors, and other drug targets (e.g., toxicity targets) are linked to inter-individual differences in the efficacy and toxicity of medications, as well as risk of genetic diseases. Furthermore, pharmacogenomics is defining factors such as heredity to explain variability of drug responses.[2]

Quality of Life and Functioning

An additional concern for patients involves the tradeoffs and comparative costs of different treatment alternatives in spheres related to mid/post-treatment life experience, such as quality of life and functionality (ability of patients to perform a spectrum of functions, such as working, driving, self-care, etc.). Since these aspects often bear more importance to patients than the mere maximization of life expectancy, substantiating the predicted outcomes of treatment alternatives with projections in these areas fosters greater patient comprehension of the relative strengths and drawbacks of each treatment alternative.

Economic Costs

Another important component of personalized medicine is a definition of the relative financial impacts of alternate treatment decisions. This analysis is best accomplished by providing patients with insight into the comparative costs of treatment options and different providers of care. In addition, different treatment options often have associated complications that could reasonably occur at different rates depending on patient specific factors, disease progression and provider experience.   Providing information on all of the cost associated with a treatment plan is a critical element to support patient-centric and value-based care decisions. The weight of this aspect of analysis is especially important in the current market, given the prevalence of high-deductible insurance plans.

Real-world Outcomes

No individual-source of currently accessible information supports the creation of comprehensive personalized treatment plans based on defining the comparative effectiveness of clinical procedures and medical technology and the heterogeneity of patient characteristics.

Clinical studies are frequently designed to determine efficacy rather than effectiveness. Clinical trials determine whether an intervention produces the expected result under ideal circumstances. Effectiveness trials measure the degree of beneficial effect under “real world” clinical settings. Thus, the deficiencies of clinical study data in supporting individualized treatment plans stems from a restrictiveness of their focal scope that reduces their applicability for patients outside of the selected study group. Trials characteristically incorporate participants who are younger, healthier, and less diverse than typical real-world patient populations, and results do not factor in influential components such as practice variation.

Claims data is another foundational component of current outcomes information. Claims data is designed to based on procedural and billing codes that are not granular enough to effectively gauge cause-and-effect relationships between treatments and outcomes. In financial spheres, this deficiency is augmented by variations in hospital costing systems and reporting capabilities. Electronic health records are an additional outlet for analysis, but since they are designed explicitly for billing purposes and not for outcomes analysis, their utility is limited.

Aside from the technical and structural aspects of information inadequacies, a lack of trust persists between health industry entities that limits data sharing between constituents, limiting the evidence base.

The result is that healthcare providers and their patients do not have access to the relevant and case-level clinical information required for personalized care. As a solution to the lack of generalizability and applicability of clinical trials results to individual cancer patients, Elting et al[3] encourage the use of population-based trials of effectiveness among “all comers.” The study of the outcomes of patients with similar cancers treated in their natural clinical setting, with suitable attention to the fundamental clinical and prognostic distinctions among different patients, will provide patients and physicians with the critical information needed for truly informed clinical decision making.

Intensely Personal Decisions

The process of combining relevant information wit advances in analytics and computing power is providing new information and enhancing medical evidence. As progressive as advances in science and technology have been, the complexities of the real world exceed what can be adequately addressed, and the variations between individual patients, real human experiences, values, and situations require multiple forms of information and analytics to converge in supporting personalized decisions. Of these factors,

different elements are of variegated levels of importance, based upon a specific individual’s’ preferences and values. Healthcare decisions are among the most intimate and personal choices we make, and therefore reveal differences in basic, fundamental values. In the context of critical decisions, patients do not wish for their values to be merely tolerated, but want their values to authentically prevail.

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Comprehensive Personalization and its Potential

Personalized medicine is revolutionizing the way we provide care. The convergence of real-world outcomes data and advanced analytic/economic factors is breaking boundaries and forcing decision-making inquiries into new spheres. By integrating patient-specific factors, disease-specific factors, quality of life considerations, and economic outcomes analysis into the process of arriving at intensely personal and impactful treatment decisions, personalized medicine holds the potential to truly transform treatment.

PotentiaMED Outcomes and Insights to Support Personalized Healthcare

At its core personalized medicine is about empowering informed individual choices based upon a patient’s’ values and personal judgments about what is important to them and their family. These are human decisions that are so complex, and multidimensional, that they transcend the capabilities of technology alone to make determinations. Technology, however, can provide the critical information necessary to empower individuals to make informed decisions.

At PotentiaMED, we embrace the complexity associated with optimizing outcomes in multiple dimensions of patient experience, through enhancing medical evidence with real-world outcomes data and advanced analytics platforms, to support personalized and value-based care decisions. The company has developed unique and proprietary analytic models that define the importance of patient specific factors, including age, diagnosis, and comorbidity, to determine important metrics, such as ones related to prognosis.

PotentiaMED Outcomes and Insights

PotentiaMED products support systematic comparisons of results and create a coherent vision to support learning, improvement, informed decisions, and optimal outcomes.

Conclusion

Patients are different in many ways, yet our current system is largely based on adherence to treatment protocols based on a select group of patients. The existing clinical standards do not adequately address many questions, including the type of patients who should undergo a specific procedure and a given individual’s risk of complications. Personalization is the future of healthcare. Through new outcomes information and transformational analytical tools, the extraordinarily complex interplay between patient variations, treatment outcomes, and tradeoffs can be captured in ways that support patient-centric care. Personalized medicine strongly enhances the power of patients to make the intensely personal decisions that will profoundly shape the subsequent chapters of their lives.

Contact

PotentiaMED

Austin (headquarters): 3755 S. Capital of Texas Hwy Suite 145, Austin, TX 78704

512.708.9000 | info@potentiamed.com

[1] Fisher, Wennberg, Stukel, et al, 2003

[2] Ventola, C. Lee. “Role of Pharmacogenomic Biomarkers In Predicting and Improving Drug Response: Part 1: The Clinical Significance of Pharmacogenetic Variants.” Pharmacy and Therapeutics 38.9 (2013): 545–560. Print.

[3] Elting LS, Cooksley C, Bekele BN et al. Generalizability of cancer clinical trial results: prognostic differences between participants and nonparticipants. Cancer. 2006;106:2452-2458.