Predictive risk stratification model: a randomised stepped-wedge trial in primary care (PRISMATIC)
Background: With a higher proportion of older people in the UK population, new approaches are needed to reduce emergency hospital admissions, thereby shifting care delivery out of hospital when possible and safe. Study aim: To evaluate the introduction of predictive risk stratification in primary ca...
| Main Authors: | , , , , , , , , , , , , , , , , , , , , , , |
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| Format: | Article |
| Language: | English |
| Published: |
National Institute for Health Research
2018-01-01
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| Series: | Health Services and Delivery Research |
| Subjects: | |
| Online Access: | https://doi.org/10.3310/hsdr06010 |
| _version_ | 1818478260141424640 |
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| author | Helen Snooks Kerry Bailey-Jones Deborah Burge-Jones Jeremy Dale Jan Davies Bridie Evans Angela Farr Deborah Fitzsimmons Jane Harrison Martin Heaven Helen Howson Hayley Hutchings Gareth John Mark Kingston Leo Lewis Ceri Phillips Alison Porter Bernadette Sewell Daniel Warm Alan Watkins Shirley Whitman Victoria Williams Ian T Russell |
| author_facet | Helen Snooks Kerry Bailey-Jones Deborah Burge-Jones Jeremy Dale Jan Davies Bridie Evans Angela Farr Deborah Fitzsimmons Jane Harrison Martin Heaven Helen Howson Hayley Hutchings Gareth John Mark Kingston Leo Lewis Ceri Phillips Alison Porter Bernadette Sewell Daniel Warm Alan Watkins Shirley Whitman Victoria Williams Ian T Russell |
| author_sort | Helen Snooks |
| collection | DOAJ |
| description | Background: With a higher proportion of older people in the UK population, new approaches are needed to reduce emergency hospital admissions, thereby shifting care delivery out of hospital when possible and safe. Study aim: To evaluate the introduction of predictive risk stratification in primary care. Objectives: To (1) measure the effects on service usage, particularly emergency admissions to hospital; (2) assess the effects of the Predictive RIsk Stratification Model (PRISM) on quality of life and satisfaction; (3) assess the technical performance of PRISM; (4) estimate the costs of PRISM implementation and its effects; and (5) describe the processes of change associated with PRISM. Design: Randomised stepped-wedge trial with economic and qualitative components. Setting: Abertawe Bro Morgannwg University Health Board, south Wales. Participants: Patients registered with 32 participating general practices. Intervention: PRISM software, which stratifies patients into four (emergency admission) risk groups; practice-based training; and clinical support. Main outcome measures: Primary outcome – emergency hospital admissions. Secondary outcomes – emergency department (ED) and outpatient attendances, general practitioner (GP) activity, time in hospital, quality of life, satisfaction and costs. Data sources: Routine anonymised linked health service use data, self-completed questionnaires and staff focus groups and interviews. Results: Across 230,099 participants, PRISM implementation led to increased emergency admissions to hospital [ΔL = 0.011, 95% confidence interval (CI) 0.010 to 0.013], ED attendances (ΔL = 0.030, 95% CI 0.028 to 0.032), GP event-days (ΔL = 0.011, 95% CI 0.007 to 0.014), outpatient visits (ΔL = 0.055, 95% CI 0.051 to 0.058) and time spent in hospital (ΔL = 0.029, 95% CI 0.026 to 0.031). Quality-of-life scores related to mental health were similar between phases (Δ = –0.720, 95% CI –1.469 to 0.030); physical health scores improved in the intervention phase (Δ = 1.465, 95% CI 0.774 to 2.157); and satisfaction levels were lower (Δ = –0.074, 95% CI – 0.133 to –0.015). PRISM implementation cost £0.12 per patient per year and costs of health-care use per patient were higher in the intervention phase (Δ = £76, 95% CI £46 to £106). There was no evidence of any significant difference in deaths between phases (9.58 per 1000 patients per year in the control phase and 9.25 per 1000 patients per year in the intervention phase). PRISM showed good general technical performance, comparable with existing risk prediction tools (c-statistic of 0.749). Qualitative data showed low use by GPs and practice staff, although they all reported using PRISM to generate lists of patients to target for prioritised care to meet Quality and Outcomes Framework (QOF) targets. Limitations: In Wales during the study period, QOF targets were introduced into general practice to encourage targeting care to those at highest risk of emergency admission to hospital. Within this dynamic context, we therefore evaluated the combined effects of PRISM and this contemporaneous policy initiative. Conclusions: Introduction of PRISM increased emergency episodes, hospitalisation and costs across, and within, risk levels without clear evidence of benefits to patients. Future research: (1) Evaluation of targeting of different services to different levels of risk; (2) investigation of effects on vulnerable populations and health inequalities; (3) secondary analysis of the Predictive Risk Stratification: A Trial in Chronic Conditions Management data set by health condition type; and (4) acceptability of predictive risk stratification to patients and practitioners. Trial and study registration: Current Controlled Trials ISRCTN55538212 and PROSPERO CRD42015016874. Funding: The National Institute for Health Research Health Services Delivery and Research programme. |
| first_indexed | 2024-12-10T09:45:38Z |
| format | Article |
| id | doaj.art-ceefb79286bf4fa0914ca1b7c5332bbc |
| institution | Directory Open Access Journal |
| issn | 2050-4349 2050-4357 |
| language | English |
| last_indexed | 2024-12-10T09:45:38Z |
| publishDate | 2018-01-01 |
| publisher | National Institute for Health Research |
| record_format | Article |
| series | Health Services and Delivery Research |
| spelling | doaj.art-ceefb79286bf4fa0914ca1b7c5332bbc2022-12-22T01:53:50ZengNational Institute for Health ResearchHealth Services and Delivery Research2050-43492050-43572018-01-016110.3310/hsdr0601009/1801/1054Predictive risk stratification model: a randomised stepped-wedge trial in primary care (PRISMATIC)Helen Snooks0Kerry Bailey-Jones1Deborah Burge-Jones2Jeremy Dale3Jan Davies4Bridie Evans5Angela Farr6Deborah Fitzsimmons7Jane Harrison8Martin Heaven9Helen Howson10Hayley Hutchings11Gareth John12Mark Kingston13Leo Lewis14Ceri Phillips15Alison Porter16Bernadette Sewell17Daniel Warm18Alan Watkins19Shirley Whitman20Victoria Williams21Ian T Russell22Swansea University Medical School, Swansea, UKAbertawe Bro Morgannwg University Health Board, Port Talbot, UKAbertawe Bro Morgannwg University Health Board, Port Talbot, UKWarwick Medical School, University of Warwick, Coventry, UKIndependent service userSwansea University Medical School, Swansea, UKSwansea Centre for Health Economics, Swansea University, Swansea, UKSwansea Centre for Health Economics, Swansea University, Swansea, UKPublic Health Wales, Cardiff, UKThe FARR Institute, Swansea University Medical School, Swansea, UKBevan Commission, School of Management, Swansea University, Swansea, UKSwansea University Medical School, Swansea, UKNHS Wales Informatics Service, Cardiff, UKSwansea University Medical School, Swansea, UKInternational Foundation for Integrated Care, Oxford, UKSwansea Centre for Health Economics, Swansea University, Swansea, UKSwansea University Medical School, Swansea, UKSwansea Centre for Health Economics, Swansea University, Swansea, UKHywel Dda University Health Board, Hafan Derwen, Carmarthen, UKSwansea University Medical School, Swansea, UKIndependent service userSwansea University Medical School, Swansea, UKSwansea University Medical School, Swansea, UKBackground: With a higher proportion of older people in the UK population, new approaches are needed to reduce emergency hospital admissions, thereby shifting care delivery out of hospital when possible and safe. Study aim: To evaluate the introduction of predictive risk stratification in primary care. Objectives: To (1) measure the effects on service usage, particularly emergency admissions to hospital; (2) assess the effects of the Predictive RIsk Stratification Model (PRISM) on quality of life and satisfaction; (3) assess the technical performance of PRISM; (4) estimate the costs of PRISM implementation and its effects; and (5) describe the processes of change associated with PRISM. Design: Randomised stepped-wedge trial with economic and qualitative components. Setting: Abertawe Bro Morgannwg University Health Board, south Wales. Participants: Patients registered with 32 participating general practices. Intervention: PRISM software, which stratifies patients into four (emergency admission) risk groups; practice-based training; and clinical support. Main outcome measures: Primary outcome – emergency hospital admissions. Secondary outcomes – emergency department (ED) and outpatient attendances, general practitioner (GP) activity, time in hospital, quality of life, satisfaction and costs. Data sources: Routine anonymised linked health service use data, self-completed questionnaires and staff focus groups and interviews. Results: Across 230,099 participants, PRISM implementation led to increased emergency admissions to hospital [ΔL = 0.011, 95% confidence interval (CI) 0.010 to 0.013], ED attendances (ΔL = 0.030, 95% CI 0.028 to 0.032), GP event-days (ΔL = 0.011, 95% CI 0.007 to 0.014), outpatient visits (ΔL = 0.055, 95% CI 0.051 to 0.058) and time spent in hospital (ΔL = 0.029, 95% CI 0.026 to 0.031). Quality-of-life scores related to mental health were similar between phases (Δ = –0.720, 95% CI –1.469 to 0.030); physical health scores improved in the intervention phase (Δ = 1.465, 95% CI 0.774 to 2.157); and satisfaction levels were lower (Δ = –0.074, 95% CI – 0.133 to –0.015). PRISM implementation cost £0.12 per patient per year and costs of health-care use per patient were higher in the intervention phase (Δ = £76, 95% CI £46 to £106). There was no evidence of any significant difference in deaths between phases (9.58 per 1000 patients per year in the control phase and 9.25 per 1000 patients per year in the intervention phase). PRISM showed good general technical performance, comparable with existing risk prediction tools (c-statistic of 0.749). Qualitative data showed low use by GPs and practice staff, although they all reported using PRISM to generate lists of patients to target for prioritised care to meet Quality and Outcomes Framework (QOF) targets. Limitations: In Wales during the study period, QOF targets were introduced into general practice to encourage targeting care to those at highest risk of emergency admission to hospital. Within this dynamic context, we therefore evaluated the combined effects of PRISM and this contemporaneous policy initiative. Conclusions: Introduction of PRISM increased emergency episodes, hospitalisation and costs across, and within, risk levels without clear evidence of benefits to patients. Future research: (1) Evaluation of targeting of different services to different levels of risk; (2) investigation of effects on vulnerable populations and health inequalities; (3) secondary analysis of the Predictive Risk Stratification: A Trial in Chronic Conditions Management data set by health condition type; and (4) acceptability of predictive risk stratification to patients and practitioners. Trial and study registration: Current Controlled Trials ISRCTN55538212 and PROSPERO CRD42015016874. Funding: The National Institute for Health Research Health Services Delivery and Research programme.https://doi.org/10.3310/hsdr06010primary careemergency health servicesclinical prediction rulerisk predictionchronic diseasestepped wedge |
| spellingShingle | Helen Snooks Kerry Bailey-Jones Deborah Burge-Jones Jeremy Dale Jan Davies Bridie Evans Angela Farr Deborah Fitzsimmons Jane Harrison Martin Heaven Helen Howson Hayley Hutchings Gareth John Mark Kingston Leo Lewis Ceri Phillips Alison Porter Bernadette Sewell Daniel Warm Alan Watkins Shirley Whitman Victoria Williams Ian T Russell Predictive risk stratification model: a randomised stepped-wedge trial in primary care (PRISMATIC) Health Services and Delivery Research primary care emergency health services clinical prediction rule risk prediction chronic disease stepped wedge |
| title | Predictive risk stratification model: a randomised stepped-wedge trial in primary care (PRISMATIC) |
| title_full | Predictive risk stratification model: a randomised stepped-wedge trial in primary care (PRISMATIC) |
| title_fullStr | Predictive risk stratification model: a randomised stepped-wedge trial in primary care (PRISMATIC) |
| title_full_unstemmed | Predictive risk stratification model: a randomised stepped-wedge trial in primary care (PRISMATIC) |
| title_short | Predictive risk stratification model: a randomised stepped-wedge trial in primary care (PRISMATIC) |
| title_sort | predictive risk stratification model a randomised stepped wedge trial in primary care prismatic |
| topic | primary care emergency health services clinical prediction rule risk prediction chronic disease stepped wedge |
| url | https://doi.org/10.3310/hsdr06010 |
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