DEFINING TODAY’S DIGITAL PATIENT

DEFINING TODAY’S DIGITAL PATIENT

Helping prevent a suicide. Connecting the elderly. Tracking the responders. Today, care communication and delivery mean more than just what occurs within the hospital. For the vulnerable, the elderly, or the military, there’s a wider range for what “healthcare” means – especially once patient care goes digital.

Connecting the patient to the health provider has become a basic in patient engagement, and industry innovators are using this core value to redefine care today. Providers and patients now have access to hundreds of thousands of apps, wearables, and medical devices through which they can track both quantitative data (vitals, activity tracking, and biometrics) and qualitative data (patient reported outcomes or mood). The collection and sharing of this information allows for the automation of a continuous exchange and a more holistic view of the patient.

Though this data on its own can provide valuable information regarding a patient’s health and mental status, for improved health insight, you need to take it further than that. More specifically, when providers can monitor patients to keep them connected to their care team — and to engage them in their own care — they can help co-produce better health and avoid adverse health events.

Subjective insights collected from patients and veterans have been invaluable. And, pairing this information with other qualitative and quantitative data representative of the patient and his or her health form a “fingerprint” that uniquely represents the patient. This allows providers or other stakeholders to review the patient’s information to communicate, intervene, and adapt treatment accordingly.

This approach differs from population health or fixed-communication programs: rather than forcing each patient to fit within a prescribed treatment regimen or population health program, the exchanges can be personalized.

Now that most providers and consumers agree that there is no one-size-fits-all approach to healthcare, these personalized approaches are critical to ensuring prolonged patient engagement in programs and overall health and wellbeing. And, these use cases are expanding, as researchers and clinicians are discovering new applications for these digital health innovations.

Data Science and Mental Health

In 2016, researchers at the Innovations for Clinical Neuroscience believed that the collection and analysis of large amounts of population and/or patient data could digitally identify high-risk individuals and suicidal behavior.

Arshya Vahabzadeh, MD, Ned Sahin, PhD, and Amir Kalali, MD conducted the 2016 study, which included everyone from soldiers and veterans (the high-risk group for suicide) to students. Their research suggested that if more behavioral or clinical data are collected, the highest-risk individuals within the population could be identified. Then, these people could receive suicide prevention services first, with treatment customized to their specific risk profiles, saving many more lives in the process.

“By collecting and analyzing large amounts of population and/or patient data, technology that digitally predicts suicides could help us to objectively quantify 1) the general impact of each of the many different risk factors and how they interact, 2) the specific risk profile of a given individual, and 3) an individual’s instantaneous risk at any given moment with consideration of any newly acquired information. Such a technology would help us more effectively target our limited resources across a much larger population. While these prospective interventions may seem optimistic, a range of research is giving us hope that they could be possible,” said the authors of “Digital Suicide Prevention: Can Technology Become a Game-Changer?”

In that same study, the authors hoped that a personalized real-time mental state and suicide risk model could be developed through the use of data analytics and cloud computing. Researchers saw that with the onset of wearable devices years ago that already enable remote monitoring, a system that will give real-time feedback to the user, psychiatrists, and other clinicians will follow.

The authors embarked on the study in seeing rapid advances in data science that can provide useful tools for suicide prevention and help to dynamically assess suicide risk in quantitative, data-driven ways. This is critical as suicide has become a significant public health issue and continues to be a leading cause of death.

“Today’s multitude of wireless devices may help us finally improve the bleak statistics and subjective clinical approaches we’ve utilized thus far in suicide prevention,” the study concluded. Today, health-focused platforms like LifeWIRE’s Smart Navigator Platform, which collects not only quantitative but also qualitative patient data, are aiming to provide the solution.

There are many risk factors leading up to thoughts of suicide, where intervention can help prevent escalation, redirect the patient, and save lives. Recent clinical insight suggests a high correlation of suicide with immediate periods (10 to 14 days) of sporadic limited sleep and wide mood swings. Perhaps knowing just about sleep and mood and acting on this information can help prevent a suicidal event.

To know of and react to this critical moment, multiple data types are needed to determine the best interventions. For example, sleep data from a patient’s wearable device, and mood data through patient-reported responses can be compared and analyzed to determine patterns, changes in patterns, and dissonance between the wearable and interactions; then, predictive modeling can be used. Even a patient’s lack of response or engagement with their health monitoring device or program could be a sign of risk. As a result, providers can be alerted, and interventions can be initiated.

Other examples of the breadth of this approach include the military and elderly.

Support for Command and Control 

Military Command and Control (C&C) needs to have ongoing, automated contact with service members to know the location, readiness, and physical state of each individual. Using automated messaging, C&C can have continuous, monitored, interactive outreach and insight to and from all active responders. Responders’ wearables and/or smart phone can query for movement, their wellness and physical status, including their blood pressure, breathing, and heart rate. Automated text messages can be used for check-ins and for gaining insight regarding stress and alertness. Rule sets can be created to provide responders with situation-specific information, triage, and assessment so their needs can be addressed quickly. When responders are not communicating, systems can be set up to send immediate notifications to C&C. By leveraging available digital health data and tools today, C&C can ensure that responders are fully ready or have the tools to intervene when needed.

Providing Connections for the Elderly

Loneliness and depression are big problems for the elderly. To address these issues, connection is key. Through automated messaging, such as text messages, check-ins about mood and alertness can be easy and effective (for example: “How do you feel today? 1= Great, 5= Lousy”). In addition, using something as simple as an “off-the-shelf” wearable, providers can monitor sleep and activity patterns, and these data can be monitored for changes in patterns that trigger interventions, education, and connection with help and support. Rules can be set up for when a patient responds in a certain way: the system then would send notifications to a provider, loved one, or caregiver.  Similarly, non-responses can be structured to send notifications to the same or different groups.

Clear communication and data collection, in all its forms, can embrace each person’s unique qualities and subsequent health status. As this technology continues to become more invisible, it can better fit into a person’s life, so that we can work to deliver quality care and health interventions before a problem occurs, ultimately increasing the quality of a patient’s life.

(As published by Validic on October 1, 2018)

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