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Record W2320406029 · doi:10.5430/jnep.v6n6p48

Use of motivational and educational techniques in behavioral health patients to impact self-care and emergency department visit rates

2016· article· en· W2320406029 on OpenAlexvenueno aff
Saadia A. Basit, Marshall J. Getz, Heather Chung

Bibliographic record

VenueJournal of Nursing Education and Practice · 2016
Typearticle
Languageen
FieldMedicine
TopicMedication Adherence and Compliance
Canadian institutionsnot available
Fundersnot available
KeywordsEmergency departmentMotivational interviewingMedicineHealth careSubstance abuseMental healthPopulationBehavior changeInterviewFamily medicineMedical emergencyNursingPsychiatryPsychological intervention

Abstract

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Background and objective: People with mental health and substance use disorders present with multiple medical comorbidities, social and legal issues. Due to these care complexities, this patient population has high rates of hospital readmissions and emergency department (ED) visits. Patients with mental health disorders require integrated care, which is the coordination of physical and behavioral health care. These patients may benefit from various educational techniques and counseling including the Teach Back Method (TBM) and motivational interviewing (MI). Houston Methodist Hospital (HMH) Behavioral Health Transition of Care Program, a quality improvement program, utilizes educational tools and counseling techniques during inpatient and post-discharge phases to improve care coordination in patients with behavioral health conditions. One of the goals of the program is to contribute to a reduction in the behavioral health/substance abuse diagnosis ED visit rate at the participating HMH system hospitals. For the first year (April 2014 through March 2015), the goal was to reduce the behavioral health ED visit rate by 5% from baseline (October 2013 through April 2014). This paper aims to 1) provide evidence to enhance motivation and establish partnership with patients, 2) report on the Behavioral Health Transition of Care Program’s use of motivational and educational techniques, 3) describe the program’s patient demographics from June 2014 through March 2015, 4) report program performance data, and 5) report ED visit data of patients with a primary or secondary behavioral health or substance abuse diagnosis. Methods: Data for two of three HMH hospitals participating in the Behavioral Health Transition of Care Program are reported. Staff members carrying out interventions include social workers, educators, nurse practitioners, and a clinical pharmacist. Patients are eligible for inclusion in program interventions if they have a current or previous mental health or substance abuse disorder and are at high risk for readmission (determined by the Discharge Decision Support System [D2S2] conducted by the floor nurse). Social workers are consulted on high risk patients to conduct a Personal Health Record (PHR) and Morisky Medication Adherence Scale (MMAS-8) and enroll patients in the post-discharge interventions (telephone calls, home visits, or both). The clinical pharmacist is consulted on inpatients with a low MMAS-8 for coaching and medication education. After discharge, enrolled patients receive follow-up automated telephone calls. Educators call patients with post-discharge issues reported via these calls. Nurse practitioners conduct one to two home visits over the 30-day post-discharge period. Aggregate data was obtained using reports obtained for quality-improvement purposes. Descriptive statistics are reported. Results: Of the 2,330 high risk encounters at HMH and San Jacinto Methodist (SJ) over June 2014 through March 2015, the average age was 55.8 years old, 4.68% encounters were insured by Medicaid, and the average D2S2 score (range: 0-11) was 4.4. Social workers completed PHR on 73.61% of the encounters and 13.48% of the discharged encounters had home visits within 30 days after discharge. There was a 4.6% reduction in the behavioral health ED visit rate from baseline to first year. Conclusions: HMH implemented a Behavioral Health Transition of Care Program that uses MI and the TBM to facilitate in reduction of ED utilization by behavioral health patients. Although the goal of 5% ED visit rate reduction was not achieved, various contributing factors such as a high demand but limited supply of primary care providers may impact the rate.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.322
Threshold uncertainty score0.176

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

Machine scores (provisional)

The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.

Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.

Opus teacher head0.107
GPT teacher head0.497
Teacher spread0.390 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
Domainnot available
GenreEmpirical

How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".

Quick stats

Citations3
Published2016
Admission routes1
Has abstractyes

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