Building an innovative model for personalized healthcare

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Personalized healthcare is the tailoring of medical management and patient care to the individual characteristics of each patient. This is achieved by incorporating the genetic and genomic makeup of an individual and his or her family medical history, environment, health-related behaviors, culture, and values into a complete health picture that can be used to customize care. Another level of personalization, often called personalized medicine, involves the selection of drug therapy through the use of tests to determine the genes and gene interactions that can reliably predict an individual’s response to a given therapy. This white paper focuses largely on the use of personalized healthcare as a risk prediction tool.


Practitioners and consumers in today’s healthcare setting do not yet fully recognize the potential benefits of personalized healthcare (Table 11). Further, proposals for reform tend to be reactive rather than proactive. Family history is well validated as a tool to predict risk for disease, but, in some instances, genomic information may enhance risk prediction provided by family history. The trial-and-error approach now used to treat disease is costly, but genomic testing has the potential to save money through more effective use of diagnostic tests, counseling about medical management based on gene test results, and prescribing of medications.

The case for personalized healthcare: Seeking value

To fully appreciate the need to advance the adoption of personalized healthcare into the delivery of medicine, one must consider the operation of our current healthcare system and its inefficiencies in terms of delivery and cost, its imprecision in the selection of therapies, and its inability to optimize outcomes. The framework of the US healthcare system as it is now constructed is expensive, disease-directed (instead of health- and wellness-directed), fragmented, and complex. While gross domestic product (GDP) in the United States has increased by approximately 3% per year,2 the compounded growth rate of healthcare expenditures is 6.1% per year. Healthcare in the aggregate now represents 17.6% of GDP and 27% of spending by the federal government and consumes 28% of the average household’s discretionary spending, surpassed only by housing.3

Personalized healthcare can potentially address the need for value consistent with the healthcare system’s prominent share of the US economy. The growth in healthcare spending is certain to be a target of the newly created Joint Select Committee on Deficit Reduction (created by the Budget Control Act of 2011), which is tasked with deficit reduction of at least $1.5 trillion over a 10-year period.

The need to address healthcare costs has been recognized in the Patient Protection and Affordable Care Act, a central feature of which is the creation of integrated health systems that pay for value based on quality, cost containment, and consumer experience. The legislation was enacted to transform healthcare in a variety of ways to make it more sustainable. The Patient Protection and Affordable Care Act seeks to end fragmentation by expanding the use of information technology to reorganize the delivery system and to prevent errors, shifting from volume-based incentives to incentives based on performance and outcomes, and rewarding effective healthcare delivery measures and good patient outcomes.

A shift from reactive to proactive

The premise behind personalized healthcare is the potential for more efficient healthcare, with the assumption that efficiency translates to lower cost and improved patient care.

Although healthcare reform is most often referred to in the context of improving access to care through insurance coverage mandates, true healthcare reform shifts current healthcare models from the practice of reactive medicine to the practice of proactive medicine, in which the tools of personalized healthcare (ie, genetics, genomics, and other molecular diagnostics) enable not only better quality of care but also less expensive care.

Several personalized tools have long been accepted into mainstream medicine. Two examples are the family history, which is the least expensive and most available genetic evaluation tool, and ABO blood typing for safe transfusions (as ABO blood types are alleles of a gene). In fact, much of what is now considered mainstream medical management was at one time considered new. To allow further evolution of medical practice, our challenge is to open our minds to the possibility that personalized proactive medicine can improve healthcare.

The new vision: More precise management

The trial-and-error approach to treating disease is inefficient and costly. Many drugs are effective for only about 50% of patients, often leading to switching or intensification of therapy that requires multiple patient visits.

Personalized medicine considers pharmacokinetic and other characteristics in selection of drug dosages. Genomic testing has the potential to provide clearer insight into the more successful use of currently available medicines. Treatment decisions (ie, drug and drug dosage choice) made on the basis of pharmacogenomic testing should increase adherence through greater effectiveness and fewer adverse drug reactions.

A massive amount of waste is related to pharmaceutical nonadherence and noncompliance. The New England Healthcare Institute has estimated that medication nonadherence costs the healthcare system $290 billion annually.4 Methodologies targeted at individual patients to improve adherence to drug regimens could save the healthcare system a tremendous amount of money.

Cancer management as a model for personalized healthcare. Personalization of therapy is especially suited to cancer management, given that the response to nonspecific cancer chemotherapy is suboptimal in most patients yet exposes them to adverse effects.5 Large-scale sequencing of human cancer genomes is rapidly changing the understanding of cancer biology and is identifying new targets in difficult-to-treat diseases and causes of drug resistance. Applying this information can achieve cost savings by avoiding the use of treatments that are ineffective in particular patients.

Overexpression of genetic mutations renders some cancers less susceptible to certain treatments, but has opened the door to individualized molecularly guided treatment strategies. For example, among patients with non–small cell lung cancer, mutations in the epidermal growth factor receptor (EGFR) tyrosine kinase domain predict response to EGFR tyrosine kinase inhibitors, and anaplastic lymphoma kinase (ALK) inhibitors induce response in patients harboring a mutation in EML4-ALK genes. The recognition that human epidermal growth factor receptor (HER)-2 overexpression as a result of ERBB2 gene amplification occurs in as many as 20% of human breast cancers paved the way for the development of HER-2–targeted therapies. Patients with advanced colorectal cancer whose tumors express the KRAS gene mutation do not benefit from an EGFR inhibitor, whereas those with wild-type KRAS have improved survival with EGFR inhibitor treatment.6


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