A systematic review of case-mix models for home health care payment: Making sense of variation

Bulck, Anne O. E. van den; Korte, Maud H. de; Elissen, Arianne; Metzelthin, Silke; Mikkers, Misja; Ruwaard, Dirk

doi:10.1016/j.healthpol.2019.12.012

Cited by 5 publications

(13 citation statements)

References 39 publications

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“…26 However, with an importance of <25, gender and low socioeconomic status were not as important as age and impairment. The systematic review by van den Bulck et al 19 identified social environmental characteristics, physical functioning and daily functioning as important categories for case-mix Figure 5 Histogram of important predictors with an importance of 25 or higher for all ages. These plots do not contain outliers of extremely high costs (horizontal axis) since the outliers made the plots not readable.…”

Section: Discussionmentioning

confidence: 99%

“… 26 However, with an importance of <25, gender and low socioeconomic status were not as important as age and impairment. The systematic review by van den Bulck et al 19 identified social environmental characteristics, physical functioning and daily functioning as important categories for case-mix predictors. An important social environmental characteristic that was identified in our study was a household size of one person per household (ie, living alone), which was a predictor in the top 20 of both groups.…”

Section: Discussionmentioning

confidence: 99%

“…18 These characteristics are dependent on (1) predisposing factors (ie, characteristics that exist before a person's illness, such as age, gender and health beliefs), (2) enabling factors (ie, logistical aspects such as income, health insurance and travel time to available facilities), and (3) need factors (ie, the level of disability and illness as perceived by the individual). 18 Two recent studies conducted in the Dutch context focusing on predictors in home healthcare including DNC identified age and gender as predisposing factors, 17 income 17 or social environmental characteristics 19 as enabling factor, and impairment, 17 physical functioning and daily functioning 19 as need factors. Both studies focus on home healthcare, which includes social work, household care, and/or care delivered by municipals, rather than district nursing specifically.…”

Section: Strengths and Limitations Of This Studymentioning

confidence: 99%

“…Two recent studies conducted in the Dutch context focusing on predictors in home healthcare including DNC identified age and gender as predisposing factors, 17 income 17 or social environmental characteristics 19 as enabling factor, and impairment, 17 physical functioning and daily functioning 19 as need factors. Both studies focus on home healthcare, which includes social work, household care, and/or care delivered by municipals, rather than district nursing specifically.…”

Section: Introductionmentioning

confidence: 99%

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Predictors of district nursing care utilisation for community-living people in the Netherlands: an exploratory study using claims data

et al. 2021

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ObjectiveTo explore predictors of district nursing care utilisation for community-living (older) people in the Netherlands using claims data. To cope with growing demands in district nursing care, knowledge about the current utilisation of district nursing care is important.SettingDistrict nursing care as a part of primary care.ParticipantsIn this nationwide study, claims data were used from the Dutch risk adjustment system and national information system of health insurers. Samples were drawn of 5500 pairs of community-living people using district nursing care (cases) and people not using district nursing care (controls) for two groups: all ages and aged 75+ years (total N=22 000).Outcome measuresThe outcome was district nursing care utilisation and the 114 potential predictors included predisposing factors (eg, age), enabling factors (eg, socioeconomic status) and need factors (various healthcare costs). The random forest algorithm was used to predict district nursing care utilisation. The performance of the models and importance of predictors were calculated.ResultsFor the population of people aged 75+ years, most important predictors were older age, and high costs for general practitioner consultations, aid devices, pharmaceutical care, ambulance transportation and occupational therapy. For the total population, older age, and high costs for pharmaceutical care and aid devices were the most important predictors.ConclusionsPeople in need of district nursing care are older, visit the general practitioner more often, and use more and/or expensive medications and aid devices. Therefore, close collaboration between the district nurse, general practitioner and the community pharmacist is important. Additional analyses including data regarding health status are recommended. Further research is needed to provide an evidence base for district nursing care to optimise the care for those with high care needs, and guide practice and policymakers’ decision-making.

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Section: Discussionmentioning

confidence: 99%

“… 26 However, with an importance of <25, gender and low socioeconomic status were not as important as age and impairment. The systematic review by van den Bulck et al 19 identified social environmental characteristics, physical functioning and daily functioning as important categories for case-mix predictors. An important social environmental characteristic that was identified in our study was a household size of one person per household (ie, living alone), which was a predictor in the top 20 of both groups.…”

Section: Discussionmentioning

confidence: 99%

Section: Strengths and Limitations Of This Studymentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Predictors of district nursing care utilisation for community-living people in the Netherlands: an exploratory study using claims data

et al. 2021

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“…cognitive functioning and continence) and daily functioning (e.g. independence in washing and dressing) to predict homecare use [ 4 ]. However, based on these most common type of predictors, homecare case-mix models are only able to explain variance in homecare use to a limited extent (i.e.…”

Section: Introductionmentioning

confidence: 99%

Identifying client characteristics to predict homecare use more accurately: a Delphi-study involving nurses and homecare purchasing specialists

Bulck

Elissen

Metzelthin

et al. 2022

BMC Health Serv Res

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Background Case-mix based prospective payment of homecare is being implemented in several countries to work towards more efficient and client-centred homecare. However, existing models can only explain a limited part of variance in homecare use, due to their reliance on health- and function-related client data. It is unclear which predictors could improve predictive power of existing case-mix models. The aim of this study was therefore to identify relevant predictors of homecare use by utilizing the expertise of district nurses and health insurers. Methods We conducted a two-round Delphi-study according to the RAND/UCLA Appropriateness Method. In the first round, participants assessed the relevance of eleven client characteristics that are commonly included in existing case-mix models for predicting homecare use, using a 9-Point Likert scale. Furthermore, participants were also allowed to suggest missing characteristics that they considered relevant. These items were grouped and a selection of the most relevant items was made. In the second round, after an expert panel meeting, participants re-assessed relevance of pre-existing characteristics that were assessed uncertain and of eleven suggested client characteristics. In both rounds, median and inter-quartile ranges were calculated to determine relevance. Results Twenty-two participants (16 district nurses and 6 insurers) suggested 53 unique client characteristics (grouped from 142 characteristics initially). In the second round, relevance of the client characteristics was assessed by 12 nurses and 5 health insurers. Of a total of 22 characteristics, 10 client characteristics were assessed as being relevant and 12 as uncertain. None was found irrelevant for predicting homecare use. Most of the client characteristics from the category ‘Daily functioning’ were assessed as uncertain. Client characteristics in other categories – i.e. ‘Physical health status’, ‘Mental health status and behaviour’, ‘Health literacy’, ‘Social environment and network’, and ‘Other’ – were more frequently considered relevant. Conclusion According to district nurses and health insurers, homecare use could be predicted better by including other more holistic predictors in case-mix classification, such as on mental functioning and social network. The challenge remains, however, to operationalize the new characteristics and keep stakeholders on board when developing and implementing case-mix classification for homecare prospective payment.

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Utilizing the Internet of Things (IoT) to address uncertain home health care supply chain network

Salehi-Amiri

Jabbarzadeh

Hajiaghaei–Keshteli

et al. 2022

Expert Systems with Applications

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A systematic review of case-mix models for home health care payment: Making sense of variation

Cited by 5 publications

References 39 publications

Predictors of district nursing care utilisation for community-living people in the Netherlands: an exploratory study using claims data

Predictors of district nursing care utilisation for community-living people in the Netherlands: an exploratory study using claims data

Identifying client characteristics to predict homecare use more accurately: a Delphi-study involving nurses and homecare purchasing specialists

Utilizing the Internet of Things (IoT) to address uncertain home health care supply chain network

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