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Improving Client Outcomes, Reducing Healthcare Costs: Part 1

The following article is the first part of a suggested method to reduce healthcare costs and help more people stay healthy.  Look for part II next week.

This article introduces how Treatment Plans, EMR data and concepts can allow management and measurement of client states of wellness to improve client outcomes and reduce costs related to behavioral health and organizations that provide such services. The following discussion describes a model that can be developed for decision making.

The method that is proposed is based on a Markov model which represents a changing set of health states over time, where there is a known probability or rate of transition from one health state to another”. The ideas for this paper come from working with EMR systems, developing a Clinician System from the ground up and industry publications.

Upon admitting a client, mental health facilities are required to develop an initial treatment plan which is then reviewed regularly until the client can be discharged back to the community – which may not always be possible (especially with long-term care), but that is the desire. 

The model requires that probability percentages be applied to the transition from one state to another (progress or regress). It may not always be possible to use quantitative data; it may be necessary to employ time-based progress, observations and measures rather than percentages to develop improved expectations.

It is a well-known fact that more than half the costs of a client’s stay at a mental health facility can be attributed to room, board and medication. If these costs can be reduced while improving client health outcomes, healthcare organizations can potentially reduce their budgets or deploy the funds to other areas that may benefit clients.

Today, when a client is admitted, one of the activities that is required is the initiation of a treatment plan for the client. The client meets with a number of clinical staff who perform various types of assessments. Based on the problems and areas of concern that have been documented in the initial interview with the client, clinical staff meet to develop a treatment plan specific to the client and schedule follow-up meetings to discuss the client’s progress.

A treatment plan can be defined in terms of a “tree” with the following nodes:

  • A client may have been diagnosed with one or more problems.
  • Each problem may have one or more (long-term) goals
  • Each goal may have one or more (short-term) objectives
  • Each objective may require one or more Interventions
  • All of these milestones should have dates so there are targets to be met by the client with assistance from staff

Consider some of the elements mentioned above:

Problem – it should be possible to define distress levels or anxiety levels a client feels for each problem.  A general distress level for anger, for example, from high to low might be

–        Incoherent

–        Acute

–        Resistant

–        Manageable

–        Calm

It should be possible to tailor such a continuum for each problem, or simply to employ a general set of levels until they can be better defined.

Similarly, an intervention can be measured in terms of its change in state, both progressive and regressive, over time. Clinicians can decide at each measurement level whether an intervention is working for a client or if it needs to be replaced by another.

By maintaining data about progress a client makes over time, a heuristic determination can be made about the types of treatments and interventions that are beneficial for clients for certain types of problems. It may then be possible to approximate the time it takes a patient to progress from a state of being unwell to a point of wellness so that so that the time to discharge the client back to the community can be lessened.

This should result in a win-win situation for clients and healthcare organizations.

The theory behind this type of method is a Markov chain which is used to measure change from one state to another. While a Markov chain is normally stochastic, it is proposed that data should be retained that provides progressive and regressive state data so that clinical staff can employ those techniques that provide a beneficial result for the client.

This is clearly not a “one size fits all” solution, since what works for one client may not work for another in the same manner or time. However, what is important is the method or approach behind the path to wellness so that decisions that are made in treatment planning can be measured and discussed with some level of objectivity.

 

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Shami Dugal is a member of ASPA and on the SHHSA Board. He has Bachelor’s degree in Operations Research from University of Waterloo (Canada) and an MPA from Drake University (Des Moines, Iowa). He has worked in the IT industry for over 30 years including the public sector. He consults to the State of Iowa and manages several enterprise systems for them, including the Behavioral Health project. For information on his research and citations in this article, he can be reached at [email protected]

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