How Can Health Informatics Support Our Efforts with Individuals with Autism?

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I am the Clinical Director of an agency serving adults with autism. In the last few years, we have had some medically complex situations and some clients who we struggled to serve. It may be that their needs are more complex because of their age, or it may be that we are missing/failing to assess for medical comorbidities. Is there anything you can point to that might help us meet the needs of these individuals or serve as an organizational check for the quality of our care?

Andrew Shlesinger, MSW, LICSW, & Haritha Gopinathan, MS
Melmark New England

We use a variety of health informatics to guide the treatment of individuals with complex medical and behavioral profiles. Below we have described some of the ways in which these variables are tracked, as well as how the data are used to influence decisions regarding care.

Health Informatics

New technologies are continually evolving and influencing the way in which human services are delivered. One of the newer technologies with high relevance to human service provision is Health Informatics (HI). HI is a field within information technology concerned with the collection, storage, reporting, and analysis of health and medical data critical to providing quality care services. Health informatics data can be used to get a much more comprehensive picture of an individual’s needs, strengths, and status. HI can provide an early warning of issues that need to be addressed. It may also provide an additional measure of meaningful outcomes such as quality of life. For those with limited capacity to communicate directly vocally, it can also be used to broaden the information available to the team.

Improving Practice

Here at Melmark, we serve a population with complex needs. Individuals served have a variety of comorbid diagnoses including physical conditions, complications of aging, neurological conditions, and psychiatric disorders. Data on a wide variety of indices helps the team monitor and manage such conditions. Indices tracked include client weight, seizure activity, fluid intake, bowel movements, menses, sleep quality, and skin integrity.

The information is collected and tracked on a web-based, tablet-enabled application. As such, the data are integrated with other continuously collected data, including behavioral targets and learning data. The information is widely available to all members of the team, making coordination of care much easier.

The facilitation of information exchange is immediate. The staff member taking fluid data sees all the data taken thus far, including the daily total and the daily goal. The comprehensiveness of this information allows the staff member to make a data-informed decision in the moment. Hence, the system optimizes data delivery to improve client outcomes in real-time.

A key element of the system is a detailed workflow; the moment a measure is recorded, that information gets to the necessary people immediately. Workflow results in a timely, informed response. Algorithms are utilized to ensure these notifications. The system then informs allied health staff, supervisors, and others of situations requiring more assessment or intervention. As an example, the system is set to provide individualized weight alerts to nursing if the individual’s weight has changed a preset percentage over 1, 3, and 6 months. Every seizure signals an immediate alert to nursing. This level of individualization enables staff members to manage their time better as well. The multidisciplinary team members can decide the measures and schedule of notification, ensuring their participation in the planning and processes.

Organizational Goals

Reporting and graphing are fully available to the interdisciplinary team to inform intervention changes and to ensure rapid responding. Furthermore, the systematic collection of these data assists in reporting any concerns to all regulatory and other bodies. Since all the data is stored in the same database as other pieces of information, it is possible to cross-reference issues with other data indicators. For example, it may be possible to track discomfort with menses, constipation, or allergies. It may also be possible to link behavioral difficulties with medical or biological events. For example, intermittent increases in targeted behaviors may correlate with constipation, which, in turn, correlates with insufficient fluid intake. Increasing the fluid intake goal, then, may improve bowel movement quality and, ultimately, reduce behaviors.

Such analyses can also be done on organizational levels. Correlations between variables may be examined across multiple clients, sites, or programs. Trend analysis across the organization can highlight areas in need of improvement. For example, perhaps fluid intake is dramatically lower in settings with older adults, compared to settings serving younger individuals. A concerted effort can then be made at sites serving older learners. Similarly, perhaps certain behaviors across clients are associated with more health issues than others. Perhaps seasonal allergies are associated with aggression or with refusal to attend a program. Such data can help identify discomfort, can indicate a need to treat that discomfort on a preventative basis, and can ensure that staff are aware of a bio-behavioral interface that requires compassionate care. This information can help inform the leadership team about policies to improve client health and management across the organization.

One untapped potential for this data is large-scale analysis. Large volumes of data collected over many years can be modeled to support large-scale health service research. It may be possible to achieve even more relevant insights when data are explored on these levels. Such analysis could have relevance well beyond one provider.

Present and Future Implications

Health informatics is a newer technology, and its relevance is beginning to be identified in many different sectors. Within adult service provision, it provides vital information to all members of the interdisciplinary team in a continuous information exchange. In the context of caring for adults with complex needs, health informatic systems can track potential issues, alert members of the team to any concerns, and provide indices of progress over time. Its value is especially high for serving individuals whose communication challenges may impede the provision of information to providers. For example, individuals on the autism spectrum may not be able to communicate a wide variety of needs and concerns; the tracking of such information can mitigate the danger associated with low self-reports.

Ultimately, the main outcomes for serving those with special needs center on quality of life. For there to be a high quality of life, physical well-being must be maximized, comfort must be assured, and preventable issues must be avoided. Health informatics can ensure these outcomes are achieved, by providing real-time data to all relevant parties continuously.

On an organizational level, the coordination of health informatics increases communication and efficiency, and ensures that there is a comprehensive approach to any concern.

Practical implementation

How can an organization achieve these goals? In our case, a data analytics professional was able to implement such a system within our data collection. The same can be done for other, existing systems. Even in the absence of such integration, simple changes can make a big difference. Identify the variables that are important to track. Develop a system for collecting such data. Develop a system for ensuring team notification of these data. Ideally, track progress over time. Are there fewer episodes of dehydration? Are weight changes caught in earlier stages of concern? Do staff report an improved sense of communication across disciplines and about important client changes?

Back to the Question

We think it is imperative that behavior analytic organizations serve individuals with complex medical needs including comorbidities, but we also see a need for more comprehensive care. It might help to embed health informatics into clinical and organizational levels of care. Integrating such information into case reviews ensures that the biological-behavioral interaction is continually assessed and looking at these issues organizationally may help ensure that patterns requiring systemic changes are detected.

Citation for this article:

Shlesinger, A., & Gopinathan, H. (2023). How can health informatics support our work? Science in Autism Treatment, 20(11).

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How Can Health Informatics Support Our Work?