Eintrag weiter verarbeiten
Characterising and predicting persistent high-cost utilisers in healthcare: a retrospective cohort study in Singapore
Gespeichert in:
| Zeitschriftentitel: | BMJ Open |
|---|---|
| Personen und Körperschaften: | , , , , , , , , , , |
| In: | BMJ Open, 10, 2020, 1, S. e031622 |
| Format: | E-Article |
| Sprache: | Englisch |
| veröffentlicht: |
BMJ
|
| Schlagwörter: |
| author_facet |
Ng, Sheryl Hui Xian Rahman, Nabilah Ang, Ian Yi Han Sridharan, Srinath Ramachandran, Sravan Wang, Debby Dan Khoo, Astrid Tan, Chuen Seng Feng, Mengling Toh, Sue-Anne Ee Shiow Tan, Xin Quan Ng, Sheryl Hui Xian Rahman, Nabilah Ang, Ian Yi Han Sridharan, Srinath Ramachandran, Sravan Wang, Debby Dan Khoo, Astrid Tan, Chuen Seng Feng, Mengling Toh, Sue-Anne Ee Shiow Tan, Xin Quan |
|---|---|
| author |
Ng, Sheryl Hui Xian Rahman, Nabilah Ang, Ian Yi Han Sridharan, Srinath Ramachandran, Sravan Wang, Debby Dan Khoo, Astrid Tan, Chuen Seng Feng, Mengling Toh, Sue-Anne Ee Shiow Tan, Xin Quan |
| spellingShingle |
Ng, Sheryl Hui Xian Rahman, Nabilah Ang, Ian Yi Han Sridharan, Srinath Ramachandran, Sravan Wang, Debby Dan Khoo, Astrid Tan, Chuen Seng Feng, Mengling Toh, Sue-Anne Ee Shiow Tan, Xin Quan BMJ Open Characterising and predicting persistent high-cost utilisers in healthcare: a retrospective cohort study in Singapore General Medicine |
| author_sort |
ng, sheryl hui xian |
| spelling |
Ng, Sheryl Hui Xian Rahman, Nabilah Ang, Ian Yi Han Sridharan, Srinath Ramachandran, Sravan Wang, Debby Dan Khoo, Astrid Tan, Chuen Seng Feng, Mengling Toh, Sue-Anne Ee Shiow Tan, Xin Quan 2044-6055 2044-6055 BMJ General Medicine http://dx.doi.org/10.1136/bmjopen-2019-031622 <jats:sec><jats:title>Objective</jats:title><jats:p>We aim to characterise persistent high utilisers (PHUs) of healthcare services, and correspondingly, transient high utilisers (THUs) and non-high utilisers (non-HUs) for comparison, to facilitate stratifying HUs for targeted intervention. Subsequently we apply machine learning algorithms to predict which HUs will persist as PHUs, to inform future trials testing the effectiveness of interventions in reducing healthcare utilisation in PHUs.</jats:p></jats:sec><jats:sec><jats:title>Design and setting</jats:title><jats:p>This is a retrospective cohort study using administrative data from an Academic Medical Centre (AMC) in Singapore.</jats:p></jats:sec><jats:sec><jats:title>Participants</jats:title><jats:p>Patients who had at least one inpatient admission to the AMC between 2005 and 2013 were included in this study. HUs incurred Singapore Dollar 8150 or more within a year. PHUs were defined as HUs for three consecutive years, while THUs were HUs for 1 or 2 years. Non-HUs did not incur high healthcare costs at any point during the study period.</jats:p></jats:sec><jats:sec><jats:title>Outcome measures</jats:title><jats:p>PHU status at the end of the third year was the outcome of interest. Socio-demographic profiles, clinical complexity and utilisation metrics of each group were reported. Area under curve (AUC) was used to identify the best model to predict persistence.</jats:p></jats:sec><jats:sec><jats:title>Results</jats:title><jats:p>PHUs were older and had higher comorbidity and mortality. Over the three observed years, PHUs’ expenditure generally increased, while THUs and non-HUs’ spending and inpatient utilisation decreased. The predictive model exhibited good performance during both internal (AUC: 83.2%, 95% CI: 82.2% to 84.2%) and external validation (AUC: 79.8%, 95% CI: 78.8% to 80.8%).</jats:p></jats:sec><jats:sec><jats:title>Conclusions</jats:title><jats:p>The HU population could be stratified into PHUs and THUs, with distinctly different utilisation trajectories. We developed a model that could predict at the end of 1 year, whether a patient in our population will continue to be a HU in the next 2 years. This knowledge would allow healthcare providers to target PHUs in our health system with interventions in a cost-effective manner.</jats:p></jats:sec> Characterising and predicting persistent high-cost utilisers in healthcare: a retrospective cohort study in Singapore BMJ Open |
| doi_str_mv |
10.1136/bmjopen-2019-031622 |
| facet_avail |
Online Free |
| format |
ElectronicArticle |
| fullrecord |
blob:ai-49-aHR0cDovL2R4LmRvaS5vcmcvMTAuMTEzNi9ibWpvcGVuLTIwMTktMDMxNjIy |
| id |
ai-49-aHR0cDovL2R4LmRvaS5vcmcvMTAuMTEzNi9ibWpvcGVuLTIwMTktMDMxNjIy |
| institution |
DE-D275 DE-Bn3 DE-Brt1 DE-Zwi2 DE-D161 DE-Gla1 DE-Zi4 DE-15 DE-Pl11 DE-Rs1 DE-105 DE-14 DE-Ch1 DE-L229 |
| imprint |
BMJ, 2020 |
| imprint_str_mv |
BMJ, 2020 |
| issn |
2044-6055 |
| issn_str_mv |
2044-6055 |
| language |
English |
| mega_collection |
BMJ (CrossRef) |
| match_str |
ng2020characterisingandpredictingpersistenthighcostutilisersinhealthcarearetrospectivecohortstudyinsingapore |
| publishDateSort |
2020 |
| publisher |
BMJ |
| recordtype |
ai |
| record_format |
ai |
| series |
BMJ Open |
| source_id |
49 |
| title |
Characterising and predicting persistent high-cost utilisers in healthcare: a retrospective cohort study in Singapore |
| title_unstemmed |
Characterising and predicting persistent high-cost utilisers in healthcare: a retrospective cohort study in Singapore |
| title_full |
Characterising and predicting persistent high-cost utilisers in healthcare: a retrospective cohort study in Singapore |
| title_fullStr |
Characterising and predicting persistent high-cost utilisers in healthcare: a retrospective cohort study in Singapore |
| title_full_unstemmed |
Characterising and predicting persistent high-cost utilisers in healthcare: a retrospective cohort study in Singapore |
| title_short |
Characterising and predicting persistent high-cost utilisers in healthcare: a retrospective cohort study in Singapore |
| title_sort |
characterising and predicting persistent high-cost utilisers in healthcare: a retrospective cohort study in singapore |
| topic |
General Medicine |
| url |
http://dx.doi.org/10.1136/bmjopen-2019-031622 |
| publishDate |
2020 |
| physical |
e031622 |
| description |
<jats:sec><jats:title>Objective</jats:title><jats:p>We aim to characterise persistent high utilisers (PHUs) of healthcare services, and correspondingly, transient high utilisers (THUs) and non-high utilisers (non-HUs) for comparison, to facilitate stratifying HUs for targeted intervention. Subsequently we apply machine learning algorithms to predict which HUs will persist as PHUs, to inform future trials testing the effectiveness of interventions in reducing healthcare utilisation in PHUs.</jats:p></jats:sec><jats:sec><jats:title>Design and setting</jats:title><jats:p>This is a retrospective cohort study using administrative data from an Academic Medical Centre (AMC) in Singapore.</jats:p></jats:sec><jats:sec><jats:title>Participants</jats:title><jats:p>Patients who had at least one inpatient admission to the AMC between 2005 and 2013 were included in this study. HUs incurred Singapore Dollar 8150 or more within a year. PHUs were defined as HUs for three consecutive years, while THUs were HUs for 1 or 2 years. Non-HUs did not incur high healthcare costs at any point during the study period.</jats:p></jats:sec><jats:sec><jats:title>Outcome measures</jats:title><jats:p>PHU status at the end of the third year was the outcome of interest. Socio-demographic profiles, clinical complexity and utilisation metrics of each group were reported. Area under curve (AUC) was used to identify the best model to predict persistence.</jats:p></jats:sec><jats:sec><jats:title>Results</jats:title><jats:p>PHUs were older and had higher comorbidity and mortality. Over the three observed years, PHUs’ expenditure generally increased, while THUs and non-HUs’ spending and inpatient utilisation decreased. The predictive model exhibited good performance during both internal (AUC: 83.2%, 95% CI: 82.2% to 84.2%) and external validation (AUC: 79.8%, 95% CI: 78.8% to 80.8%).</jats:p></jats:sec><jats:sec><jats:title>Conclusions</jats:title><jats:p>The HU population could be stratified into PHUs and THUs, with distinctly different utilisation trajectories. We developed a model that could predict at the end of 1 year, whether a patient in our population will continue to be a HU in the next 2 years. This knowledge would allow healthcare providers to target PHUs in our health system with interventions in a cost-effective manner.</jats:p></jats:sec> |
| container_issue |
1 |
| container_start_page |
0 |
| container_title |
BMJ Open |
| container_volume |
10 |
| format_de105 |
Article, E-Article |
| format_de14 |
Article, E-Article |
| format_de15 |
Article, E-Article |
| format_de520 |
Article, E-Article |
| format_de540 |
Article, E-Article |
| format_dech1 |
Article, E-Article |
| format_ded117 |
Article, E-Article |
| format_degla1 |
E-Article |
| format_del152 |
Buch |
| format_del189 |
Article, E-Article |
| format_dezi4 |
Article |
| format_dezwi2 |
Article, E-Article |
| format_finc |
Article, E-Article |
| format_nrw |
Article, E-Article |
| _version_ |
1792339766569598986 |
| geogr_code |
not assigned |
| last_indexed |
2024-03-01T15:53:21.59Z |
| geogr_code_person |
not assigned |
| openURL |
url_ver=Z39.88-2004&ctx_ver=Z39.88-2004&ctx_enc=info%3Aofi%2Fenc%3AUTF-8&rfr_id=info%3Asid%2Fvufind.svn.sourceforge.net%3Agenerator&rft.title=Characterising+and+predicting+persistent+high-cost+utilisers+in+healthcare%3A+a+retrospective+cohort+study+in+Singapore&rft.date=2020-01-01&genre=article&issn=2044-6055&volume=10&issue=1&pages=e031622&jtitle=BMJ+Open&atitle=Characterising+and+predicting+persistent+high-cost+utilisers+in+healthcare%3A+a+retrospective+cohort+study+in+Singapore&aulast=Tan&aufirst=Xin+Quan&rft_id=info%3Adoi%2F10.1136%2Fbmjopen-2019-031622&rft.language%5B0%5D=eng |
| SOLR | |
| _version_ | 1792339766569598986 |
| author | Ng, Sheryl Hui Xian, Rahman, Nabilah, Ang, Ian Yi Han, Sridharan, Srinath, Ramachandran, Sravan, Wang, Debby Dan, Khoo, Astrid, Tan, Chuen Seng, Feng, Mengling, Toh, Sue-Anne Ee Shiow, Tan, Xin Quan |
| author_facet | Ng, Sheryl Hui Xian, Rahman, Nabilah, Ang, Ian Yi Han, Sridharan, Srinath, Ramachandran, Sravan, Wang, Debby Dan, Khoo, Astrid, Tan, Chuen Seng, Feng, Mengling, Toh, Sue-Anne Ee Shiow, Tan, Xin Quan, Ng, Sheryl Hui Xian, Rahman, Nabilah, Ang, Ian Yi Han, Sridharan, Srinath, Ramachandran, Sravan, Wang, Debby Dan, Khoo, Astrid, Tan, Chuen Seng, Feng, Mengling, Toh, Sue-Anne Ee Shiow, Tan, Xin Quan |
| author_sort | ng, sheryl hui xian |
| container_issue | 1 |
| container_start_page | 0 |
| container_title | BMJ Open |
| container_volume | 10 |
| description | <jats:sec><jats:title>Objective</jats:title><jats:p>We aim to characterise persistent high utilisers (PHUs) of healthcare services, and correspondingly, transient high utilisers (THUs) and non-high utilisers (non-HUs) for comparison, to facilitate stratifying HUs for targeted intervention. Subsequently we apply machine learning algorithms to predict which HUs will persist as PHUs, to inform future trials testing the effectiveness of interventions in reducing healthcare utilisation in PHUs.</jats:p></jats:sec><jats:sec><jats:title>Design and setting</jats:title><jats:p>This is a retrospective cohort study using administrative data from an Academic Medical Centre (AMC) in Singapore.</jats:p></jats:sec><jats:sec><jats:title>Participants</jats:title><jats:p>Patients who had at least one inpatient admission to the AMC between 2005 and 2013 were included in this study. HUs incurred Singapore Dollar 8150 or more within a year. PHUs were defined as HUs for three consecutive years, while THUs were HUs for 1 or 2 years. Non-HUs did not incur high healthcare costs at any point during the study period.</jats:p></jats:sec><jats:sec><jats:title>Outcome measures</jats:title><jats:p>PHU status at the end of the third year was the outcome of interest. Socio-demographic profiles, clinical complexity and utilisation metrics of each group were reported. Area under curve (AUC) was used to identify the best model to predict persistence.</jats:p></jats:sec><jats:sec><jats:title>Results</jats:title><jats:p>PHUs were older and had higher comorbidity and mortality. Over the three observed years, PHUs’ expenditure generally increased, while THUs and non-HUs’ spending and inpatient utilisation decreased. The predictive model exhibited good performance during both internal (AUC: 83.2%, 95% CI: 82.2% to 84.2%) and external validation (AUC: 79.8%, 95% CI: 78.8% to 80.8%).</jats:p></jats:sec><jats:sec><jats:title>Conclusions</jats:title><jats:p>The HU population could be stratified into PHUs and THUs, with distinctly different utilisation trajectories. We developed a model that could predict at the end of 1 year, whether a patient in our population will continue to be a HU in the next 2 years. This knowledge would allow healthcare providers to target PHUs in our health system with interventions in a cost-effective manner.</jats:p></jats:sec> |
| doi_str_mv | 10.1136/bmjopen-2019-031622 |
| facet_avail | Online, Free |
| format | ElectronicArticle |
| format_de105 | Article, E-Article |
| format_de14 | Article, E-Article |
| format_de15 | Article, E-Article |
| format_de520 | Article, E-Article |
| format_de540 | Article, E-Article |
| format_dech1 | Article, E-Article |
| format_ded117 | Article, E-Article |
| format_degla1 | E-Article |
| format_del152 | Buch |
| format_del189 | Article, E-Article |
| format_dezi4 | Article |
| format_dezwi2 | Article, E-Article |
| format_finc | Article, E-Article |
| format_nrw | Article, E-Article |
| geogr_code | not assigned |
| geogr_code_person | not assigned |
| id | ai-49-aHR0cDovL2R4LmRvaS5vcmcvMTAuMTEzNi9ibWpvcGVuLTIwMTktMDMxNjIy |
| imprint | BMJ, 2020 |
| imprint_str_mv | BMJ, 2020 |
| institution | DE-D275, DE-Bn3, DE-Brt1, DE-Zwi2, DE-D161, DE-Gla1, DE-Zi4, DE-15, DE-Pl11, DE-Rs1, DE-105, DE-14, DE-Ch1, DE-L229 |
| issn | 2044-6055 |
| issn_str_mv | 2044-6055 |
| language | English |
| last_indexed | 2024-03-01T15:53:21.59Z |
| match_str | ng2020characterisingandpredictingpersistenthighcostutilisersinhealthcarearetrospectivecohortstudyinsingapore |
| mega_collection | BMJ (CrossRef) |
| physical | e031622 |
| publishDate | 2020 |
| publishDateSort | 2020 |
| publisher | BMJ |
| record_format | ai |
| recordtype | ai |
| series | BMJ Open |
| source_id | 49 |
| spelling | Ng, Sheryl Hui Xian Rahman, Nabilah Ang, Ian Yi Han Sridharan, Srinath Ramachandran, Sravan Wang, Debby Dan Khoo, Astrid Tan, Chuen Seng Feng, Mengling Toh, Sue-Anne Ee Shiow Tan, Xin Quan 2044-6055 2044-6055 BMJ General Medicine http://dx.doi.org/10.1136/bmjopen-2019-031622 <jats:sec><jats:title>Objective</jats:title><jats:p>We aim to characterise persistent high utilisers (PHUs) of healthcare services, and correspondingly, transient high utilisers (THUs) and non-high utilisers (non-HUs) for comparison, to facilitate stratifying HUs for targeted intervention. Subsequently we apply machine learning algorithms to predict which HUs will persist as PHUs, to inform future trials testing the effectiveness of interventions in reducing healthcare utilisation in PHUs.</jats:p></jats:sec><jats:sec><jats:title>Design and setting</jats:title><jats:p>This is a retrospective cohort study using administrative data from an Academic Medical Centre (AMC) in Singapore.</jats:p></jats:sec><jats:sec><jats:title>Participants</jats:title><jats:p>Patients who had at least one inpatient admission to the AMC between 2005 and 2013 were included in this study. HUs incurred Singapore Dollar 8150 or more within a year. PHUs were defined as HUs for three consecutive years, while THUs were HUs for 1 or 2 years. Non-HUs did not incur high healthcare costs at any point during the study period.</jats:p></jats:sec><jats:sec><jats:title>Outcome measures</jats:title><jats:p>PHU status at the end of the third year was the outcome of interest. Socio-demographic profiles, clinical complexity and utilisation metrics of each group were reported. Area under curve (AUC) was used to identify the best model to predict persistence.</jats:p></jats:sec><jats:sec><jats:title>Results</jats:title><jats:p>PHUs were older and had higher comorbidity and mortality. Over the three observed years, PHUs’ expenditure generally increased, while THUs and non-HUs’ spending and inpatient utilisation decreased. The predictive model exhibited good performance during both internal (AUC: 83.2%, 95% CI: 82.2% to 84.2%) and external validation (AUC: 79.8%, 95% CI: 78.8% to 80.8%).</jats:p></jats:sec><jats:sec><jats:title>Conclusions</jats:title><jats:p>The HU population could be stratified into PHUs and THUs, with distinctly different utilisation trajectories. We developed a model that could predict at the end of 1 year, whether a patient in our population will continue to be a HU in the next 2 years. This knowledge would allow healthcare providers to target PHUs in our health system with interventions in a cost-effective manner.</jats:p></jats:sec> Characterising and predicting persistent high-cost utilisers in healthcare: a retrospective cohort study in Singapore BMJ Open |
| spellingShingle | Ng, Sheryl Hui Xian, Rahman, Nabilah, Ang, Ian Yi Han, Sridharan, Srinath, Ramachandran, Sravan, Wang, Debby Dan, Khoo, Astrid, Tan, Chuen Seng, Feng, Mengling, Toh, Sue-Anne Ee Shiow, Tan, Xin Quan, BMJ Open, Characterising and predicting persistent high-cost utilisers in healthcare: a retrospective cohort study in Singapore, General Medicine |
| title | Characterising and predicting persistent high-cost utilisers in healthcare: a retrospective cohort study in Singapore |
| title_full | Characterising and predicting persistent high-cost utilisers in healthcare: a retrospective cohort study in Singapore |
| title_fullStr | Characterising and predicting persistent high-cost utilisers in healthcare: a retrospective cohort study in Singapore |
| title_full_unstemmed | Characterising and predicting persistent high-cost utilisers in healthcare: a retrospective cohort study in Singapore |
| title_short | Characterising and predicting persistent high-cost utilisers in healthcare: a retrospective cohort study in Singapore |
| title_sort | characterising and predicting persistent high-cost utilisers in healthcare: a retrospective cohort study in singapore |
| title_unstemmed | Characterising and predicting persistent high-cost utilisers in healthcare: a retrospective cohort study in Singapore |
| topic | General Medicine |
| url | http://dx.doi.org/10.1136/bmjopen-2019-031622 |