Building an innovative model for personalized healthcare

INTEGRATING PERSONALIZED HEALTHCARE INTO CLINICAL PRACTICE

Practice standardization and an overhaul of the health information technology (HIT) infrastructure are needed if we are to reap the potential benefits of personalized healthcare. Creative approaches to practitioner education, which are being used in some institutions, must become more widespread. Similarly, the models for successful integration of personalized healthcare that have been achieved in some settings also can be implemented in other institutions.

Data collection and integration must be prioritized

Personalized healthcare can be both predictive and preventive, but moving past the disruptive phase of personalized healthcare will require a radical transformation of the healthcare “ecosystem” and HIT infrastructure.

Although data collection in the current system is extensive, data sharing and data management are inadequate. The pace at which HIT links clinical and genetic information must be accelerated. HIT will expedite innovation and implementation of personalized healthcare, allowing greater integration of data to permit improved data analysis capability. The ultimate goal is to create an interoperable system that connects these data across hospitals and clinicians to help clinicians interpret genomic and other risk information to better inform patient care.

Fully integrated health systems support better coordination of care and optimize the treatment of individual patients: linking research findings, treatment guidelines, treatment outcomes based on genetic profiles, and the individual patient’s own genetic profile will help to personalize treatments. Genomic information added to an individual’s electronic medical record along with improved data-sharing will facilitate clinicians’ ability to retrieve outcomes data based on patient characteristics.

Care models must be standardized, evidence-based practices must be executed, and care must be coordinated yet decentralized. In this way, clinicians can use the electronic medical record as an interoperable patient record to determine a personalized pathway to patient management. Standardization reduces variability in practice and permits seamless execution of care. Automation is imperative to achieving standardization, irrespective of the care supervisor. Investments must therefore be made to stimulate electronic medical record decision support.

In addition, larger data sets will be needed to identify the types of patients likely to respond to a treatment. Ideal data sets would be large enough to have adequate statistical power, be publicly available, standardize the collection of data with respect to response to therapy and toxicity, and contain data on concomitant collections of biologic samples.

Reimbursement must keep pace with medical advances

Payer willingness to reimburse for genomic tests and treatments will determine the pace of integration of personalized healthcare into clinical practice. Evidence that enhanced value can be derived from personalized approaches to medicine must be generated before personalized healthcare gains widespread acceptance by payers.

In addition, care-coordinated models must be developed to promote a value-based agenda that facilitates physician accountability and encourages clinical integration.

Innovative approaches are needed to educate providers

Development of point-of-care tools. Because information overload and lack of time are obstacles to clinicians’ efforts to incorporate genomic information into clinical practice, emphasis must be placed on genomic applications that have demonstrated utility. Engaging busy clinicians with point-of-care tools will maximize the relevance of the genomic information they receive and encourage effective use of their time. Decision-making should be supported through automatic risk assessment and management recommendations.

Educational tools. The National Coalition for Health Professional Education in Genetics (NCHPEG) was borne out of the recognition that the pace of genomic discovery far exceeds the pace at which healthcare providers can be educated. Its vision is to improve healthcare through informed use of genomic resources. NCHPEG is a member-based organization whose stakeholders include professional societies, hospitals, advocacy groups, and industry; it attempts to identify the specific educational needs for particular target audiences and then address these needs. It achieves its goals through the use of point-of-care tools and educational programs for continuing medical education credit.

One NCHPEG tool is the Pregnancy and Health Profile, which is a risk assessment and screening tool that attempts to improve the identification of women and babies at risk of developing genetic disease. It collects personal and family history information, performs a risk assessment for the clinician, and provides clinical decision support and education.

Another example of an educational tool is the “Genes to Society” curriculum initiated by The Johns Hopkins University School of Medicine in August 2009. The curriculum is being used as “the foundation for the scientific and clinical career development of future physicians.”¹⁵

Using personal genomic testing for education. The number of direct-to-consumer genomic tests is growing, and their market penetration will only increase as the cost of supplying a personal genome continues to decline. Whole genome scanning is being offered with the promise of identifying genetic predisposition to multiple diseases.

Participation in personal genomic testing may be a useful educational tool. Medical students, residents, and practicing physicians who participate in testing may be better equipped to advise patients about the processes involved and the potential utility and limitations of direct-to-consumer genotyping.¹⁴

Some companies that offer direct-to-consumer genomic testing provide telephone support from genetic counselors to help clients and their healthcare providers manage genetic information. Counselor services include identifying hereditary risks and reviewing diagnostic, preventive, and early-detection options.

Implementing pharmacogenomics into practice: Decision support systems are needed

A genomic decision support system that guides medication prescribing is needed to implement pharmacogenomic diagnostics. For such a system to achieve the goal of selecting the best medication for each individual, it must do the following:

• Test all polymorphisms relevant to the prescribing of any medication
• Be completed with no out-of-pocket cost to the patient
• Be performed before the patient requires the medication
• Provide results that will be interpreted as part of an individualized pharmacogenomics consult.¹¹

The 1200 Patients Project, a pilot research study under way at the Center for Personalized Therapeutics at the University of Chicago, is attempting to demonstrate the feasibility of incorporating pharmacogenomic testing into routine clinical practice for medication treatment decisions. DNA samples from patients who are taking at least one prescription medication are being tested for differences in genes that may suggest greater effectiveness or an increased risk of side effects from certain medications.