Dataset Information

Predicting high-cost care in a mental health setting.

ABSTRACT:

Background

The density of information in digital health records offers new potential opportunities for automated prediction of cost-relevant outcomes.

Aims

We investigated the extent to which routinely recorded data held in the electronic health record (EHR) predict priority service outcomes and whether natural language processing tools enhance the predictions. We evaluated three high priority outcomes: in-patient duration, readmission following in-patient care and high service cost after first presentation.

Method

We used data obtained from a clinical database derived from the EHR of a large mental healthcare provider within the UK. We combined structured data with text-derived data relating to diagnosis statements, medication and psychiatric symptomatology. Predictors of the three different clinical outcomes were modelled using logistic regression with performance evaluated against a validation set to derive areas under receiver operating characteristic curves.

Results

In validation samples, the full models (using all available data) achieved areas under receiver operating characteristic curves between 0.59 and 0.85 (in-patient duration 0.63, readmission 0.59, high service use 0.85). Adding natural language processing-derived data to the models increased the variance explained across all clinical scenarios (observed increase in r2 = 12-46%).

Conclusions

EHR data offer the potential to improve routine clinical predictions by utilising previously inaccessible data. Of our scenarios, prediction of high service use after initial presentation achieved the highest performance.

SUBMITTER: Colling C

PROVIDER: S-EPMC7001466 | biostudies-literature | 2020 Jan

REPOSITORIES: biostudies-literature

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Publications

Predicting high-cost care in a mental health setting.

Colling Craig C Khondoker Mizanur M Patel Rashmi R Fok Marcella M Harland Robert R Broadbent Matthew M McCrone Paul P Stewart Robert R

BJPsych open 20200117 1

<h4>Background</h4>The density of information in digital health records offers new potential opportunities for automated prediction of cost-relevant outcomes.<h4>Aims</h4>We investigated the extent to which routinely recorded data held in the electronic health record (EHR) predict priority service outcomes and whether natural language processing tools enhance the predictions. We evaluated three high priority outcomes: in-patient duration, readmission following in-patient care and high service co ...[more]

PMID: 31950891

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Project description:IntroductionPrevious research has shown that the socioeconomic status (SES)-health gradient also extends to high-cost patients; however, little work has examined high-cost patients with mental illness and/or addiction. The objective of this study was to examine associations between individual-, household- and area-level SES factors and future high-cost use among these patients.MethodsWe linked survey data from adult participants (ages 18 and older) of 3 cycles of the Canadian Community Health Survey to administrative health care data from Ontario, Canada. Respondents with mental illness and/or addiction were identified based on prior mental health and addiction health care use and followed for 5 years for which we ascertained health care costs covered under the public health care system. We quantified associations between SES factors and becoming a high-cost patient (i.e., transitioning into the top 5%) using logistic regression models. For ordinal SES factors, such as income, education and marginalization variables, we measured absolute and relative inequalities using the slope and relative index of inequality.ResultsAmong our sample, lower personal income (odds ratio [OR] = 2.11, 95% confidence interval [CI], 1.54 to 2.88, for CAD$0 to CAD$14,999), lower household income (OR = 2.11, 95% CI, 1.49 to 2.99, for lowest income quintile), food insecurity (OR = 1.87, 95% CI, 1.38 to 2.55) and non-homeownership (OR = 1.34, 95% CI, 1.08 to 1.66), at the individual and household levels, respectively, and higher residential instability (OR = 1.72, 95% CI, 1.23 to 2.42, for most marginalized), at the area level, were associated with higher odds of becoming a high-cost patient within a 5-year period. Moreover, the inequality analysis suggested pro-high-SES gradients in high-cost transitions.ConclusionsPolicies aimed at high-cost patients with mental illness and/or addiction, or those concerned with preventing individuals with these conditions from becoming high-cost patients in the health care system, should also consider non-clinical factors such as income as well as related dimensions including food security and homeownership.

Project description:ImportanceA higher out-of-pocket price for mental health care may lead not only to cost savings but also to negative downstream consequences.ObjectiveTo examine the association of higher patient cost sharing with mental health care use and downstream effects, such as involuntary commitment and acute mental health care use.Design, setting, and participantsThis difference-in-differences study compared changes in mental health care use by adults, who experienced an increase in cost sharing, with changes in youths, who did not experience the increase and thus formed a control group. The study examined all 2 780 558 treatment records opened from January 1, 2010, through December 31, 2012, by 110 organizations that provide specialist mental health care in the Netherlands. Data analysis was performed from January 18, 2016, to May 9, 2017.ExposuresOn January 1, 2012, the Dutch national government increased the out-of-pocket price of mental health services for adults by up to €200 (US$226) per year for outpatient treatment and €150 (US$169) per month for inpatient treatment.Main outcomes and measuresThe number of treatment records opened each day in regular specialist mental health care, involuntary commitment, and acute mental health care, and annual specialist mental health care spending.ResultsThis study included 1 448 541 treatment records opened from 2010 to 2012 (mean [SD] age, 41.4 [16.7] years; 712 999 men and 735 542 women). The number of regular mental health care records opened for adults decreased abruptly and persistently by 13.4% (95% CI, -16.0% to -10.8%; P < .001) per day when cost sharing was increased in 2012. The decrease was substantial and significant for severe and mild disorders and larger in low-income than in high-income neighborhoods. Simultaneously, in 2012, daily record openings increased for involuntary commitment by 96.8% (95% CI, 87.7%-105.9%; P < .001) and for acute mental health care by 25.1% (95% CI, 20.8%-29.4%; P < .001). In contrast to our findings for adults, the use of regular care among youths increased slightly and the use of involuntary commitment and acute care decreased slightly after the reform. Overall, the cost-sharing reform was associated with estimated savings of €13.4 million (US$15.1 million). However, for adults with psychotic disorder or bipolar disorder, the additional costs of involuntary commitment and acute mental health care exceeded savings by €25.5 million (US$28.8 million).Conclusions and relevanceHigher cost sharing for seriously ill and low-income patients could discourage treatment of vulnerable populations and create substantial downstream costs.

Project description:BackgroundIntegrating mental health services into primary care settings is complex and challenging. Although facilitation strategies have successfully supported implementation of primary care mental health integration and other complex innovations, we know little about the time required or its cost.ObjectiveTo examine the time and organizational cost of facilitating implementation of primary care mental health integration.DesignDescriptive analysis.ParticipantsOne expert external facilitator and two internal regional facilitators who helped healthcare system stakeholders, e.g., leaders, managers, clinicians, and non-clinical staff, implement primary care mental health integration at eight clinics.InterventionImplementation facilitation tailored to the needs and resources of the setting and its stakeholders.Main measuresWe documented facilitators' and stakeholders' time and types of activities using a structured spreadsheet collected from facilitators on a weekly basis. We obtained travel costs and salary information. We conducted descriptive analysis of time data and estimated organizational cost.Key resultsThe external facilitator devoted 263 h (0.09 FTE), including travel, across all 8 clinics over 28 months. Internal facilitator time varied across networks (1792 h versus 1169 h), as well as clinics. Stakeholder participation time was similar across networks (1280.6 versus 1363.4 person hours) but the number of stakeholders varied (133 versus 199 stakeholders). The organizational cost of providing implementation facilitation also varied across networks ($263,490 versus $258,127). Stakeholder participation accounted for 35% of the cost of facilitation activities in one network and 47% of the cost in the other.ConclusionsAlthough facilitation can improve implementation of primary care mental health integration, it requires substantial organizational investments that may vary by site and implementation effort. Furthermore, the cost of using an external expert to transfer facilitation skills and build capacity for implementation efforts appears to be minimal.

Dataset Information

Predicting high-cost care in a mental health setting.

Background

Aims

Method

Results

Conclusions

Publications

Predicting high-cost care in a mental health setting.

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