Predicting patient engagement in IAPT services: a statistical analysis of electronic health records
Background Across England, 12% of all improving access to psychological therapy (IAPT) appointments are missed, and on average around 40% of first appointments are not attended, varying significantly around the country. In order to intervene effectively, it is important to target the patients who ar...
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| Format: | Article |
| Language: | English |
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BMJ Publishing Group
2020-02-01
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| Series: | BMJ Mental Health |
| Online Access: | https://mentalhealth.bmj.com/content/23/1/8.full |
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| _version_ | 1823857253548032000 |
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| author | David Betts Alice Davis Theresa Smith Jenny Talbot Chris Eldridge |
| author_facet | David Betts Alice Davis Theresa Smith Jenny Talbot Chris Eldridge |
| author_sort | David Betts |
| collection | DOAJ |
| description | Background Across England, 12% of all improving access to psychological therapy (IAPT) appointments are missed, and on average around 40% of first appointments are not attended, varying significantly around the country. In order to intervene effectively, it is important to target the patients who are most likely to miss their appointments.Objective This research aims to develop and test a model to predict whether an IAPT patient will attend their first appointment.Methods Data from 19 adult IAPT services were analysed in this research. A multiple logistic regression was used at an individual service level to identify which patient, appointment and referral characteristics are associated with attendance. These variables were then used in a generalised linear mixed effects model (GLMM). We allow random effects in the GLMM for variables where we observe high service to service heterogeneity in the estimated effects from service specific logistic regressions.Findings We find that patients who self-refer are more likely to attend their appointments with an OR of 1.04. The older a patient is, the fewer the number of previous referrals and consenting to receiving a reminder short message service are also found to increase the likelihood of attendance with ORs of 1.02, 1.10, 1.04, respectively.Conclusions Our model is expected to help IAPT services identify which patients are not likely to attend their appointments by highlighting key characteristics that affect attendance.Clinical implications This analysis will help to identify methods IAPT services could use to increase their attendance rates. |
| format | Article |
| id | doaj-art-096ebb5c383a417d8cfa6643b0be889d |
| institution | Kabale University |
| issn | 2755-9734 |
| language | English |
| publishDate | 2020-02-01 |
| publisher | BMJ Publishing Group |
| record_format | Article |
| series | BMJ Mental Health |
| spelling | doaj-art-096ebb5c383a417d8cfa6643b0be889d2025-02-11T22:00:10ZengBMJ Publishing GroupBMJ Mental Health2755-97342020-02-0123110.1136/ebmental-2019-300133Predicting patient engagement in IAPT services: a statistical analysis of electronic health recordsDavid Betts0Alice Davis1Theresa Smith2Jenny Talbot3Chris Eldridge41 Department of Mathematical Sciences, University of Bath, Bath, UK1 Department of Mathematical Sciences, University of Bath, Bath, UK1 Department of Mathematical Sciences, University of Bath, Bath, UK1 Department of Mathematical Sciences, University of Bath, Bath, UK1 Department of Mathematical Sciences, University of Bath, Bath, UKBackground Across England, 12% of all improving access to psychological therapy (IAPT) appointments are missed, and on average around 40% of first appointments are not attended, varying significantly around the country. In order to intervene effectively, it is important to target the patients who are most likely to miss their appointments.Objective This research aims to develop and test a model to predict whether an IAPT patient will attend their first appointment.Methods Data from 19 adult IAPT services were analysed in this research. A multiple logistic regression was used at an individual service level to identify which patient, appointment and referral characteristics are associated with attendance. These variables were then used in a generalised linear mixed effects model (GLMM). We allow random effects in the GLMM for variables where we observe high service to service heterogeneity in the estimated effects from service specific logistic regressions.Findings We find that patients who self-refer are more likely to attend their appointments with an OR of 1.04. The older a patient is, the fewer the number of previous referrals and consenting to receiving a reminder short message service are also found to increase the likelihood of attendance with ORs of 1.02, 1.10, 1.04, respectively.Conclusions Our model is expected to help IAPT services identify which patients are not likely to attend their appointments by highlighting key characteristics that affect attendance.Clinical implications This analysis will help to identify methods IAPT services could use to increase their attendance rates.https://mentalhealth.bmj.com/content/23/1/8.full |
| spellingShingle | David Betts Alice Davis Theresa Smith Jenny Talbot Chris Eldridge Predicting patient engagement in IAPT services: a statistical analysis of electronic health records BMJ Mental Health |
| title | Predicting patient engagement in IAPT services: a statistical analysis of electronic health records |
| title_full | Predicting patient engagement in IAPT services: a statistical analysis of electronic health records |
| title_fullStr | Predicting patient engagement in IAPT services: a statistical analysis of electronic health records |
| title_full_unstemmed | Predicting patient engagement in IAPT services: a statistical analysis of electronic health records |
| title_short | Predicting patient engagement in IAPT services: a statistical analysis of electronic health records |
| title_sort | predicting patient engagement in iapt services a statistical analysis of electronic health records |
| url | https://mentalhealth.bmj.com/content/23/1/8.full |
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