Predictors of length of stay in a psychiatric hospital

Gopalakrishna, Ganesh; Ithman, Muaid; Malwitz, Kari

doi:10.3109/13651501.2015.1062522

Cited by 40 publications

(40 citation statements)

References 32 publications

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“…Our results supported previous studies that have demonstrated a signi cant impact of psychosis on length of stay and a negligible impact of substance use disorders and personality disorders on length of stay [10,11,12] . While international studies often demonstrate reduced length of stay in detained patients [5] , our ndings support UK studies that show the use of the Mental Health Act is related to longer admissions [13] , perhaps suggesting compulsory detention is used differently in the NHS.…”

Section: Multiple Regression Analysissupporting

confidence: 92%

“…Unlike other studies, we did not nd an association between homelessness and length of stay [13,14] but we did nd a similarly signi cant impact of the requirement for referral to accommodation on discharge. In keeping with the con icting data surrounding age and length of stay, we were unable to demonstrate any signi cant association [12] .…”

Section: Multiple Regression Analysiscontrasting

confidence: 54%

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Patient and Service-Level Factors Affecting Length of Inpatient Stay in an Acute Mental Health Service: A Retrospective Case Cohort Study

Crossley

Sweeney

2020

Preprint

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Background: The NHS Mental Health Implementation Plan aims to reduce length of inpatient psychiatric stays to a maximum of 32 days, yet provides little guidance on how to achieve this. Previous studies have attempted to analyse factors influencing length of stay in mental health units, focussing mostly on patient factors. These models fail to sufficiently explain the variation in duration of inpatient stay. We assess how the type of service delivered by a trust, in addition to patient factors, influences length of stay.Methods: We conducted a retrospective case cohort study in a large inner-city NHS mental health trust for all admissions in a one month period. Data was gathered from electronic notes of 105 patients. Descriptive univariate and bivariate analyses were conducted on the data, with multiple regression analysis conducted on statistically significant data. Results: Short-stay assessment ward admission significantly reduced length of stay. Patients under outpatients or under care co-ordination, admitted through Mental Health Act assessment and formally detained all had longer length of stay. Out of area admissions, locum Consultant care, changing Responsible Clinician and ward transfers all led to longer length of stay. Factors indicating more severe illness such as increased observation level and polypharmacy, as well as diagnoses of psychosis or bipolar disorder were associated with longer duration of stay. Discharges requiring referral to accommodation or rehabilitation led to longer stays. The most significant factors that influenced length of stay were higher observation levels, diagnosis of psychotic illness or bipolar, and discharge to rehabilitation placement. The final model, taking into account all these factors, was able to account for 59.6% of the variability in length of stay.Conclusions: The study backs up existing literature which shows patient-factors have an influence on length of stay. The study also demonstrates that service-level factors have an impact on the duration of stay. This data may be used to inform further studies which may aid provision of inpatient and community services in the future.

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Section: Multiple Regression Analysissupporting

confidence: 92%

Section: Multiple Regression Analysiscontrasting

confidence: 54%

Patient and Service-Level Factors Affecting Length of Inpatient Stay in an Acute Mental Health Service: A Retrospective Case Cohort Study

Crossley

Sweeney

2020

Preprint

View full text Add to dashboard Cite

show abstract

“…Unlike other studies, we did not find an association between homelessness and length of stay [15,16] but we did find a similarly significant impact of the requirement for referral to accommodation on discharge. In keeping with the conflicting data surrounding age and length of stay, we were unable to demonstrate any significant association [14].…”

Section: Discussioncontrasting

confidence: 61%

Patient and service-level factors affecting length of inpatient stay in an acute mental health service: a retrospective case cohort study

Crossley

Sweeney

2020

BMC Psychiatry

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Background The NHS Mental Health Implementation Plan aims to reduce length of inpatient psychiatric stays to a maximum of 32 days, yet provides little guidance on how to achieve this. Previous studies have attempted to analyse factors influencing length of stay in mental health units, focussing mostly on patient factors. These models fail to sufficiently explain the variation in duration of inpatient stay. We assess how the type of service delivered by a trust, in addition to patient factors, influences length of stay. Methods We conducted a retrospective case cohort study in a large inner-city NHS mental health trust for all admissions in a 1 month period. Data was gathered from electronic notes of 105 patients. Descriptive univariate and bivariate analyses were conducted on the data, with multiple regression analysis conducted on statistically significant data. Results Short-stay assessment ward admission significantly reduced length of stay. Patients under outpatients or under care co-ordination, admitted through Mental Health Act assessment and formally detained all had longer length of stay. Out of area admissions, locum Consultant care, changing Responsible Clinician and ward transfers all led to longer length of stay. Factors indicating more severe illness such as increased observation level and polypharmacy, as well as diagnoses of psychosis or bipolar disorder were associated with longer duration of stay. Discharges requiring referral to accommodation or rehabilitation led to longer stays. The most significant factors that influenced length of stay were higher observation levels, diagnosis of psychotic illness or bipolar, and discharge to rehabilitation placement. The final model, taking into account all these factors, was able to account for 59.6% of the variability in length of stay. Conclusions The study backs up existing literature which shows patient-factors have an influence on length of stay. The study also demonstrates that service-level factors have an impact on the duration of stay. This data may be used to inform further studies which may aid provision of inpatient and community services in the future.

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“…The development and protocol of this case register have been described in detail elsewhere. 10,11 The predictor variables were assembled based on candidates listed in previous reviews [6][7][8][9]12 and following project meetings that had representation from psychiatrists, researchers, informaticians and National Health Service (NHS) managers. Structured data from the EHR included patient demographics, service use, and health and social functioning measured by the Health of the Nation Outcome Scales.…”

Section: Settingmentioning

confidence: 99%

Predicting high-cost care in a mental health setting

et al. 2020

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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.

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Predictors of length of stay in a psychiatric hospital

Cited by 40 publications

References 32 publications

Patient and Service-Level Factors Affecting Length of Inpatient Stay in an Acute Mental Health Service: A Retrospective Case Cohort Study

Patient and Service-Level Factors Affecting Length of Inpatient Stay in an Acute Mental Health Service: A Retrospective Case Cohort Study

Patient and service-level factors affecting length of inpatient stay in an acute mental health service: a retrospective case cohort study

Predicting high-cost care in a mental health setting

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