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Personalised approach towards precision medicine

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Kent Lefner, Partner-Healthcare, Insurance and Life Sciences, Infosys and Bhargava Hukunda, Principal Consultant-Healthcare, Insurance and Life Sciences, Infosys elaborate on the need for new data solutions to unlock the full potential of precision medicine

A one-size-fits-all approach to medicine doesn’t work for every patient because factors like genetics, environment and lifestyle can impact the effectiveness of the treatment.
The need for a personalised approach has given rise to precision medicine, which focusses on the individual, but not in the way one would think. Rather than designing treatments on a case-by-case basis, greater efficacy has been found by placing patients into subgroups based on characteristics that predict the effectiveness of a treatment.

Big-data thinking in medicine can help practitioners better understand the factors that influence the disease, the nuanced evolution of that disease, improve diagnostic accuracy and optimise treatment plans. Most hospital and physician groups are not yet ready to support this form of analytics.

The field of precision medicine holds tremendous promise, but there are challenges standing in the way of wider adoption. The first is the time it takes to analyse data about the population, create subgroups for whom a treatment plan can be applied and then evaluated, and then determine the probability of success. For each subgroup, data is being collected from thousands of patients, which is a massive undertaking. Our understanding of the human genome is still in its infancy and evolving every day. Progress has been made, but so much is still unknown. Getting it right will take time.

Another challenge is data sharing. It’s often difficult to acquire the expansive data that exists between provider, payer, pharmaceutical and medical device companies to coalesce into one body of knowledge. The industry is changing. Change is happening, but it’s slow and incremental. A regulatory framework will also be needed to address how data is shared between parties, as each link in the medical chain will want a say in the decision-making process.

Cost is another challenge. It’s expensive to study human
genomics, and not everybody is willing to spend the full extent of the money required to do that. Many payers, for example, still view genomics and precision medicine as too expensive and too experimental for them to apply significant funding against, creating a barrier to success.

A new, data-centered solution
Unlocking the full potential of precision medicine starts with finding better ways to collect, share and make decisions based on data. Current solutions aren’t optimal in this area because data is siloed in the various healthcare segments.

Platforms need to handle high volumes of data and produce actionable insights. By doing so, they can offer tremendous benefits to participants in the healthcare ecosystem.

Successful implementations haven’t happened on a wider scale because of distractions and varying priorities. Most participants in the healthcare industry are focussed on issues surrounding chronic care management, higher patient efficacy, interactive enablement, competitiveness and competitive disruption response. Breaking through their list of priorities and convincing them to invest money in this kind of solution could prove challenging.
A second barrier to success is the willingness of the ecosystem to share the vastness of data. Creating that ecosystem won’t be easy, but with the power of a member genome solution, the process becomes manageable.

Who will invest in the future of precision medicine?
To adopt this type of system in a precision medicine context means starting with data. The sad reality is that, due to competition and lack of government regulations driving cooperative capabilities, an industry-wide data exchange does not exist within healthcare. However, this should not stop interested parties from taking steps to adopt solutions powered by AI and automation that can maximise their efforts in the field of precision medicine.

The first step is to identify the different dimensions of data that will be required and pulling together what’s available to create a baseline. Creating a data baseline would allow an organisation to jumpstart any precision medicine initiatives because once that data is available, it can be input into a member genome framework that has all the architecture components and analytic algorithm libraries in place. Beforehand, it will be important for organisations to define the parameters of this pilot effort by looking at source systems, derived attributes and data transformation, as well as target visualisation and analytical model specs. A member genome solution should have a pre-fabricated set of gene blocks and data models across all dimensions of a health plan including member, customer, plan benefits, providers, lifestyle, behavioural and customer service.

A robust solution should include all three parameters of the Think, Build, Run paradigm. Beyond the platform itself, companies should think through their strategy and convert that strategy into action. The build components make that action a reality. The run components can operate the business on behalf of clients at more cost-effective levels than competitors.

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