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The effect of patient, provider and financing regulations on the intensity of ambulatory physical therapy episodes: a multilevel analysis based on routinely available data.

Halfon P, Eggli Y, Morel Y, Taffé P - BMC Health Serv Res (2015)

Bottom Line: We sought to quantify the contribution of patients and providers to these differences and examine how effective Swiss regulations are (nine-session ceiling per prescription and bonus for first treatments).Regulations were the most powerful factors.Moreover, further research is needed on the determinants of patient demand.

View Article: PubMed Central - PubMed

Affiliation: Institute of Social and Preventive Medicine (IUMSP), University Hospital Center and Faculty of Biology and Medicine, Biopole 2, Route de la Corniche 10, 1010, Lausanne, Switzerland. patricia.halfon@chuv.ch.

ABSTRACT

Background: Many studies have found considerable variations in the resource intensity of physical therapy episodes. Although they have identified several patient- and provider-related factors, few studies have examined their relative explanatory power. We sought to quantify the contribution of patients and providers to these differences and examine how effective Swiss regulations are (nine-session ceiling per prescription and bonus for first treatments).

Methods: Our sample consisted of 87,866 first physical therapy episodes performed by 3,365 physiotherapists based on referrals by 6,131 physicians. We modeled the number of visits per episode using a multilevel log linear regression with crossed random effects for physiotherapists and physicians and with fixed effects for cantons. The three-level explanatory variables were patient, physiotherapist and physician characteristics.

Results: The median number of sessions was nine (interquartile range 6-13). Physical therapy use increased with age, women, higher health care costs, lower deductibles, surgery and specific conditions. Use rose with the share of nine-session episodes among physiotherapists or physicians, but fell with the share of new treatments. Geographical area had no influence. Most of the variance was explained at the patient level, but the available factors explained only 4% thereof. Physiotherapists and physicians explained only 6% and 5% respectively of the variance, although the available factors explained most of this variance. Regulations were the most powerful factors.

Conclusion: Against the backdrop of abundant physical therapy supply, Swiss financial regulations did not restrict utilization. Given that patient-related factors explained most of the variance, this group should be subject to closer scrutiny. Moreover, further research is needed on the determinants of patient demand.

No MeSH data available.


Related in: MedlinePlus

Hierarchical and cross-classified data structure.
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Fig3: Hierarchical and cross-classified data structure.

Mentions: The data had a hierarchical and cross-classified structure, with patients (level 1) nested within the cells of the cross-classification of physicians and physiotherapists (both at level 2, as physicians had ongoing relationships with several physiotherapists, and physiotherapists with multiple physicians, see Figure 3). Physicians and physiotherapists were nested within cantons (level 3). We therefore used a multilevel regression model with crossed random effects for physiotherapists and physicians and with fixed effects for cantons [29,30]. We assessed potentially explanatory variables at their various levels (see Figure 3). One quarter of patients had multiple episodes over time; adjusting for these correlations would dramatically increase modeling complexity [31]. We therefore restricted our analyses to the first episode per patient. Although this strategy did not use all the information contained in the data, it remained effective thanks to the large sample size and the time-invariant explanatory factors.Figure 3


The effect of patient, provider and financing regulations on the intensity of ambulatory physical therapy episodes: a multilevel analysis based on routinely available data.

Halfon P, Eggli Y, Morel Y, Taffé P - BMC Health Serv Res (2015)

Hierarchical and cross-classified data structure.
© Copyright Policy - open-access
Related In: Results  -  Collection

License 1 - License 2
Show All Figures
getmorefigures.php?uid=PMC4325958&req=5

Fig3: Hierarchical and cross-classified data structure.
Mentions: The data had a hierarchical and cross-classified structure, with patients (level 1) nested within the cells of the cross-classification of physicians and physiotherapists (both at level 2, as physicians had ongoing relationships with several physiotherapists, and physiotherapists with multiple physicians, see Figure 3). Physicians and physiotherapists were nested within cantons (level 3). We therefore used a multilevel regression model with crossed random effects for physiotherapists and physicians and with fixed effects for cantons [29,30]. We assessed potentially explanatory variables at their various levels (see Figure 3). One quarter of patients had multiple episodes over time; adjusting for these correlations would dramatically increase modeling complexity [31]. We therefore restricted our analyses to the first episode per patient. Although this strategy did not use all the information contained in the data, it remained effective thanks to the large sample size and the time-invariant explanatory factors.Figure 3

Bottom Line: We sought to quantify the contribution of patients and providers to these differences and examine how effective Swiss regulations are (nine-session ceiling per prescription and bonus for first treatments).Regulations were the most powerful factors.Moreover, further research is needed on the determinants of patient demand.

View Article: PubMed Central - PubMed

Affiliation: Institute of Social and Preventive Medicine (IUMSP), University Hospital Center and Faculty of Biology and Medicine, Biopole 2, Route de la Corniche 10, 1010, Lausanne, Switzerland. patricia.halfon@chuv.ch.

ABSTRACT

Background: Many studies have found considerable variations in the resource intensity of physical therapy episodes. Although they have identified several patient- and provider-related factors, few studies have examined their relative explanatory power. We sought to quantify the contribution of patients and providers to these differences and examine how effective Swiss regulations are (nine-session ceiling per prescription and bonus for first treatments).

Methods: Our sample consisted of 87,866 first physical therapy episodes performed by 3,365 physiotherapists based on referrals by 6,131 physicians. We modeled the number of visits per episode using a multilevel log linear regression with crossed random effects for physiotherapists and physicians and with fixed effects for cantons. The three-level explanatory variables were patient, physiotherapist and physician characteristics.

Results: The median number of sessions was nine (interquartile range 6-13). Physical therapy use increased with age, women, higher health care costs, lower deductibles, surgery and specific conditions. Use rose with the share of nine-session episodes among physiotherapists or physicians, but fell with the share of new treatments. Geographical area had no influence. Most of the variance was explained at the patient level, but the available factors explained only 4% thereof. Physiotherapists and physicians explained only 6% and 5% respectively of the variance, although the available factors explained most of this variance. Regulations were the most powerful factors.

Conclusion: Against the backdrop of abundant physical therapy supply, Swiss financial regulations did not restrict utilization. Given that patient-related factors explained most of the variance, this group should be subject to closer scrutiny. Moreover, further research is needed on the determinants of patient demand.

No MeSH data available.


Related in: MedlinePlus