Can Patient-Generated Data Help Your Treatment Goals Be S.M.A.R.T ?

As clinicians, we are often challenged to make our goals and objectives as specific as possible. The people that oversee our services want to see how we are measuring treatment progress. Satisfying auditors and regulators is a critical part of our work. Even more important is to be clear to clients about what outcomes we are working towards together. Determining goals and developing an agreed-upon self-tracking means to measure progress while meeting regulatory needs can be challenging. Self tracking and measuring symptoms, also known as Patient-Generated Health Data (PGHD) may help.

Enter the acronym of treatment goals and objectives being S.M.A.R.T:






For example, working on an anger management goal, it’s not always simple as “Client X will stop getting angry”. There needs to be a way to demonstrate that there is progress for both clients and regulators. To make this goal more “SMART” we might convert that anger management goal to something like “In the next 3 months Client X will reduce angry outbursts to at least once per week”. This makes it more specific, measurable and time-limited.

It gets tricky determining what is both attainable and realistic. This decision is more nuanced and based on knowledge of previous behaviors. For instance, if the client is aggressive 4 times a week, displaying no aggression in a matter of weeks might not be realistic or attainable for that individual. This may be a setup for failure and make it challenging to reach that goal. This requires a partnership between clients and providers. One tool to make goals S.M.A.R.T’er is Patient-Generated Health Data (PGHD). In this example, a parent can use a health app to track the frequency of aggression so that they can report this to the clinician. This may also include information about what was happening before and after the outburst.

Lorden et. al. (2020) did a systematic review of how PGHD from health apps impacted the client and provider relationship. Researchers noted that clients wanted their PGHD from health apps to be used in clinical encounters. They felt that it increased trust and communication when discussing the data. Researchers noted that both patients and clinicians felt like the data served as an educational tool that helped in goal attainment. Four studies noted that clients were able to make behavioral changes as a result of the PGHD without clinician interpretation. However, barriers were noted; both clinicians and patients often struggled to make the data understandable and actionable. Clinicians struggled with where to put the data in their clinical encounters citing time constraints and lack of being reimbursed for their time.

At Awake Labs, we attempt to mitigate some of these barriers with our technology by using multiple forms of data visualization and formats for clinicians and our mobile app for staff/caregivers. We pair biometric data collected from our smartwatch software with qualitative data entered in the mobile app by caregivers.

Time series graph that shows fluctuating levels of strong emotions
Time-series graph of strong emotions

Visualizations can be a means to make data further specific, measurable, attainable, and realistic. This provides a means to see the data during an encounter in a meaningful way to hopefully reduce some of the barriers noted above. With a clear display of trends, clinicians, family, and other caregivers can co-create a way to both understand the data and measure progress. Also, data can be used to see trends to define how attainable or realistic a goal may be over time.

A heatmap showing dark green to dark red in a gradient over a week to indicate levels of stress at a specific hour.
Heatmap of strong emotions

Currently, we use heatmaps to display full days, weeks, and months' worth of data. For shorter periods, like hours, we use time-series graphs. We aim to complement these time series graphs with qualitative data collected through responses to escalation notifications delivered via the mobile app. Qualitative data can be some of the most difficult data to obtain because when the person you support is escalated it is unlikely you have the time to observe and document your surroundings before intervening. We are actively working with our users to find the best ways of displaying data for their needs.

This is how we envision the use of Patient-Generated Health Data. Would love to hear how you might use PGHD as a means to set goals and objectives! Please comment below or comment on Twitter or LinkedIn.