An algorithm is designed to assess readiness for change.
Eating disorder treatment occurs in a variety of settings, and determining the appropriate level of care is a key clinical decision. Treatment guidelines and (in some health-care environments) insurer decisions play roles in making this determination. An alternative, algorithmic approach, The Short Treatment Allocation Tool for Eating Disorders (STATED), was recently tested in a study of 179 healthcare professionals (J Eat Disord. 2018; 6:45).
The STATED algorithm uses three patient dimensions, medical stability, symptom severity/life interference, and readiness/engagement, to assign the level of care for patients with eating disorders. Medical stability, defined as the patient’s immediate risk, is the only information needed to determine if a patient should be hospitalized, while Symptom severityis used to determine whether a patient needs a higher degree of care, such as day, residential, or inpatient treatment, or outpatient treatment with inpatient support. Readinessis used to determine higher or lower treatment options and the focus of treatment. In fact, inclusion of readiness as a main component of the STATED algorithm was added after 20 years of research showed the importance of readiness for change and the role it plays in predicting improvement of symptoms.
Dr. Josie Geller and colleagues at the Eating Disorders Program, St. Paul’s Hospital, and the University of British Columbia, Vancouver, noted that concordance between clinical decisions made and the results of the STATED was generally high, most notably for medical stability. The greatest discordance was between clinical decision-making and the STATED in regard to readiness for change. When a stringent coding system was used, high levels of inconsistency were detected in readiness; this affected 58% of patients and 66% of families. Possible explanations include a lack of understanding of the implications of low readiness, and the absence of validated measures of readiness such as lack of alternatives to action-oriented treatment, for example, the quality of life for individuals who are very ill. The authors reported that when a stringent coding system was used, high levels of inconsistency were detected in readiness; this affected 58% of patients and 66% of families. Possible explanations include a lack of understanding of the implications of low readiness, and the absence of validated measures of readiness such as lack of alternatives to action-oriented treatment, for example, the quality of life for individuals who are very ill.
Improving readiness
The authors argue that many patients and families need help in understanding that action-oriented treatment is not helpful when the patient and his or her family do not see themselves as having a problem (defined as low readiness). One potential step way to help clinicians improve assessment of patient readiness is training assessors to use a collaborative/motivational interviewing style. Another helpful step would be providing clear program guidelines with characteristics for each level of care. Finally, the Canadian researchers suggest that such guidelines could facilitate communication among patients, clinicians, and carers.
