Patients Are Ready for Personalized Health Care, and the Data Delivers

Targeted Therapies in OncologyFebruary I, 2024
Volume 13
Issue 2
Pages: 21

Devices like smart blood pressure cuffs, scales, and even smartphones with health sensors are turning our daily routines into personalized health insights.

The consumer experience of the digital world is steeped in personalization. Digital health technology has the potential to do the same for our wellness. Internet of Things (IoT) devices such as internet-connected blood pressure cuffs, weight scales, and glucometers are revolutionizing risk identification in health care. Even smartphone sensors can provide insights into an individual’s health, seamlessly integrating into daily routines.

One of the significant advantages of IoT technology in health care is its ability to help clinicians swiftly and accurately identify risks, sharing real-time data with other stakeholders to improve care coordination and personalize treatments. Here is how it is reshaping the landscape:

Continuous monitoring – A smartphone can collect heart rate variability and respiratory rate without active user engagement. Patients can use these devices at their convenience, independent of a clinician’s availability, and they outperform traditional methods in the amount of data collected. When connected to health care professionals through electronic health records or data networks, these devices provide a more comprehensive view of patients than that gained during in-office visits. They also offer immediate insights into deviations from established health care thresholds that might otherwise go unnoticed, creating the opportunity for more effective intervention.

Statistical analysis – Collecting more data more frequently might seem burdensome, but data analysis tools resolve that concern. Machine learning algorithms are a highly advanced option to recognize patterns, but even simpler algorithms are effective. With plenty of computing power available from large cloud service providers to assist, a rules engine, regression-based analysis, or basic statistical analysis can sift through vast datasets and uncover subtle patterns and anomalies.

Predictive models – These tools can detect early signs of health issues that traditional methods might miss, reducing the workload for health care professionals by automating data analysis and surfacing actionable insights. They can also be used to develop predictive models for anticipating health risks. By analyzing historical data and trends, health care professionals and payers can gain better insight into population health and can intervene proactively with targeted resources and benefi ts to encourage healthy behaviors preventing adverse health outcomes.

With smart device-generated data and analytics, physicians can leverage a patient’s data repository to guide them on a care path tailored to their preferences and behaviors, without the time-consuming process of manual data analysis.

This cloud-based data repository can serve as the hub connecting all stakeholders, with the patient at the center. Physicians can make medication adjustments, health plans can suggest resources for sourcing healthy food, nutritionists can recommend dietary changes, and so on.

Perhaps most significant for patients, this kind of personalized solution can help stop the progression of their condition. For example, an estimated 70% of individuals with prediabetes will eventually develop type 2 diabetes if they do not take steps to manage the condition.1

Personalization is a path forward for the health care industry to prioritize the patient experience rather than approaching problems with a one-size-fits-all approach. When health care evolves to incorporate the vetted lessons from tech industries, patients benefit. Data-driven risk identification enables holistic care by shifting the focus to include the individual patient’s circumstances, not simply the health complication. It represents a transformative shift toward personalized, proactive health care, driven by the power of data and technology.

By harnessing data from mobile and connected devices and employing advanced analytics, we can develop smarter systems to identify risks and initiate timely interventions. When implemented on a broader scale, such an approach can also address some of the significant access-to-care challenges prevalent in today’s maternal health landscape.

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Tabák AG, Herder C, Rathmann W, Brunner EJ, Kivimäki M. Prediabetes: a high-risk state for diabetes development. Lancet. 2012;379(9833):2279-2290. doi:10.1016/S01406736(12)60283-9
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