Precision Medicine for High-Need, High-Cost Patients (HNHC): What Needs to Happen

in #steemstem5 years ago (edited)

A Scientific Journal Article by researchers, Khullar and Kaushal (2018), attempts to address an important health concern in today’s medical world- how health systems can better assist HNHC patients. HNHC patients represent a vast majority of those needing diverse and unique medical care. This group of patients also makes up an enormous proportion of those utilizing large amounts of healthcare, which means high health expenditures. Meeting the needs of such patients is very difficult due to the immense amount of collaboration needed among physicians and health systems. In the U.S., healthcare tends to be economically driven, thus data-sharing is sometimes halted due to fear of losing patients and, in turn, economic incentives. Currently, most up-to-date analyses have focused primarily on claims data. While claims data is important in addressing the needs of HNHC patients, it is also important to collect information regarding clinical factors, genomic data as well as social determinants across health systems (Khullar & Kaushal, 2018).

A number of limitations exist, restricting precision medicine from arising at the forefront of healthcare. First, as previously mentioned, data is not always shared between hospitals and across health systems. For example, hospitals that are regionally concentrated in an urban location are likely competing for patients to utilize their services. In these competitive settings, hospitals are much less likely to perform data-sharing efforts in fear of potentially losing patients. Another limitation are drivers of healthcare utilization. Payer type and insurance coverage differs quite drastically in the U.S., and specific drivers of coverage may enable some patients to utilize healthcare more than others. Also, certain conditions may have specialized clinical guidelines for treatment. For instance, mental health concerns differ drastically from chronic conditions, and require specific intervention approaches rather than specialized medical treatment. Costs and spending is also different between patients and their unique conditions. This gray area may result in a hesitation of moving away from what is comfortable, to entirely foreign methods and unconventional approaches. Lastly, claims data has been the primary focus for gathering data, but this method alone fails to capture important data for treating HNHC patients effectively. Claims data may actually underestimate the prevalence and magnitude of certain illnesses. A focus on claims data in tandem with clinical and genomic factors may actually improve treatment methods since not every individual responds the same to medications and dosage levels (Khullar & Kaushal, 2018).

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In order to address these concerns, multiple health systems must begin to incorporate large, structured databases that share essential information. Using genetic information in combination with social determinant data would allow for more precise diagnoses. With precision medicine, physicians will be able to match symptoms against a database that precisely target a specific condition. The condition would then be treated with a more personalized approach for that particular individual. Such databases would potentially enable more accurate predictions about patients needing specific treatment, and with healthcare delivery models this would allow better treatment for certain subgroups. Also, improving the reallocation of resources may enhance patient outcomes as well as reduce healthcare expenditures as a result. For precision health, a comprehensive understanding of how clinical and social factors affect populations differently is vitally important in terms of addressing health disparities. For example, how does lack of access to care or unstable social conditions deferentially influence care, costs, and outcomes for patients of varying socioeconomic statuses? There are a number of obstacles that remain, but improved health and lower costs will require a more precise approach. Multidimensional networks and care models must be adopted in order to better predict patient needs, care delivery, and put targeted approaches in action (Khullar & Kaushal, 2018).

Reference:

Khullar, D., & Kaushal, R. (2018). “Precision Health” for High-Need, High-Cost Patients. The American Journal of Managed Care, 24(9), 396-398.

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A shared public database combined with ML algorithms could drastically improve health care. I really liked this discussion and I think the sooner we take steps towards it the better.

Most definitely. Especially with the use of big data and public health. Professionals can use these techniques to emulate randomized controlled trials to save money and find out what works, and what doesn’t

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