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How to Transform Patient Care with Predictive Health Diagnostics

Healthcare organizations, whether in countries where the state shoulders the cost, or where this is left to individuals and insurers, are under pressure to improve outcomes and reduce costs.

One of the ways the healthcare sector can keep innovating, improving outcomes and reducing costs is with predictive health diagnostics and analytics. Other innovations that are being talked about excitedly in the healthcare sector are AI, machine learning (ML), business intelligence (BI) and a range of other new technology solutions that are centered around similar goals.

According to Lauren Neal, a principal at Booz Allen Hamilton, predictive analytics is a way of “identify[ing] pain points throughout the stages of intake and care to improve both healthcare delivery and patient experience.”

A Society of Actuaries study found that 93% of healthcare organizations “say predictive analytics is important to the future of their business, with 89 percent of providers currently using predictive analytics or planning to do so in the next five years.”

Examples of Predictive Health Diagnostics

Predictive diagnostics is a way of using large amounts of data to provide more accurate outcomes.

Take kidney disease, for example. In the U.S., 20 percent of Medicare’s budget goes on various treatments, after-care, and diagnostics. With predictive diagnostics analyzing millions of previous cases, it will improve and increase the number being treat earlier, and therefore reduce how many Americans develop end-stage renal disease.

It isn’t just kidney disease. Predictive analytics could be used in primary and early-stage healthcare too, to assess and analyze lifestyle choices and help doctors to identify patients early-on who would benefit from dietary and fitness changes. Prevention is always better than cure.

In developed and developing countries, lifestyle and fitness choices are often an accurate predictor of health problems. Therefore, the sooner governments and healthcare organizations can identify and help to mitigate risks, the easier it will be to reduce the impact on providers and payers.

Healthcare has always been from birth to death. Whether a patient passes away naturally or the result of a diseases, palliative care is often needed when this happens in a hospital or hospice. End-of-life care is difficult to deliver, which is why predictive health diagnostics is a way of augmenting and supporting the judgement of doctors and other healthcare providers. Penn Medicine started to use predictive analytics in 2017 to launch a system known as Palliative Connect, which uses 30 different factors to more accurately predict a patients prognosis over 6-months.

In 2018, The Journal of General Medicine published the results of the Palliative Connect trial project. Based in one of Penn Medicine’s hospitals, between December 2017 and February 2018, 85 patients were identified for palliative care consultations compared to 22 under normal circumstance. A massive 74 percent increase, clearly demonstrating the need for predictive health diagnostic analytics.

Many other studies have shown similar promising results, ensuring that trials are rolled forward and help many more patients.

How Healthcare Companies Can Introduce Predictive Diagnostics?

Here are five steps for introducing predictive healthcare diagnostics in medical areas where it has yet to be rolled out. If this is new to your organization, then working with an IT and digital partner with expertise in this and related fields could make this process significantly easier and more cost and operationally effective.

1. Identify Treatments That Would Benefit

Predictive health diagnostics are already playing an important role in healthcare, across many areas from treatment through to drug development. If there are treatments or other services that would benefit, start with a closer look at the statistics. Look at what needs improving. Especially when it comes to patient outcomes.

2. Make the Business Case

Using big data and machine learning, analyze where the human element would benefit from the extra analytical support. Predictive health diagnostics improve speed, accuracy, and reduce errors. If those are outcomes your organization needs, then there is going to be a business case outlining the upside of introducing predictive diagnostics.

3. Be Clear on the Costs and ROI

Working with an IT and digital partner with expertise in this and related fields, you should be able to get a clear idea of the potential cost and ROI (savings made, and improved patient outcomes, etc). A project such as this rolled out the right way should have multiple benefits. Costs will reduce, workloads made easier to manage, and a whole load of new efficiencies and innovations, alongside improved patient care.

4. Pilot Program

As with anything medical, a trial project, or pilot program to test the impact and workability in practice is required. Do this over a limited timescale, with a large enough group of patients or data to make it statistically useful. It’s also a useful opportunity to see how it will work in practice and therefore resolve any potential challenges.

5. Full Rollout

With a pilot program implemented successfully, now is the time to pick a rollout date and plan that will ensure hundreds or thousands of patients benefit. As part of this, educational materials need to be prepared for service users and providers. When patients are directly involved this can be a larger scale process.

However, when the end-users are doctors and medical teams, the rollout process needs to involve educational sessions. Especially when they are being asked to use a new piece of technology that will potentially change an existing process. Training and support are therefore essential parts of the rollout process, ensuring that everyone gains the benefits of a new predictive health diagnostics system.

Conclusion

In a medical setting, the introduction of anything new takes time. Healthcare organizations are incredibly innovative, but for the sake and safety of patients and other stakeholders, new systems and processes need to be rolled-out slowly and carefully. Once that has been worked through, the full benefits of predictive health diagnostics system will be felt by patients, payers and healthcare providers.

Dariya Lopukhina
Dariya Lopukhina is a technology enthusiast and writer. She writes about digital innovations, tech trends and entrepreneurship.

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