Parallel_Session_1_Talk_5_Tondelli

Thursday, September 12, 2013

September 13, 2013 | Tribschenstrasse 21, 6002 Luzern

Reimbursement Reform and the Distribution of Labor between In- and Outpatient Care: Evidence from Switzerland

Timo Tondelli Swiss Health Economics Workshop


T. Tondelli | Swiss Health Economics Workshop | Sep. 13, 2013 2

Swiss Health Economics Workshop

Reimbursement Reform and the Distribution of Labor between In- and Outpatient Care: Evidence from Switzerland Author: Timo Tondelli PhD Student & Research Associate Supervisor: Prof. Dr. Stefan Felder WWZ, Universität Basel Health Economics

[email protected]



Contribution

1

2

Theoretical model that clarifies the role of specific reimbursement schemes on physicians’ decision how to allocate medical in- and outpatient inputs in the production of patient health.

Empirical evidence on the impact of inpatient reimbursement on the distribution of labor between in- and outpatient care by exploiting a quasi-experiment in Switzerland.



Results at a glance

Theoretical prediction:

• Increase of outpatient inputs if the proportional share of reimbursement in inpatient care is reduced.

• Prospective hospital payment (DRG) is expected to increase the amount of ambulatory care related to inpatient stays.

Empirical findings:

• Empirical evidence supports theoretical prediction: A less effective cost based inpatient reimbursement increases ambulatory care linked to inpatient stays – shift of treatment parts.

• Patient data of a major Swiss Health insurance company.

• Ambulatory care is measured by the number of physician visits and billed taxpoints.

• Swiss DRG introduction offers a quasi-experiment.



1. Literature | Theoretical Model | Swiss Inpatient Reimbursement

2. Data Sample & Empirical Strategy

3. Empirical Findings & Conclusion

Index



Literature review

• Discharges to nursing homes rise significantly after DRG introduction in the USA (Sloan and Valvona (1986), Sager et al. (1987, 1989)).

• Increase in outpatient physician visits in years after DRG introduction in USA (Hadley et al. (1987), Menke (1990)). Visits to physician offices not considered and no link between inpatient an outpatient care on patient level.

• No evidence for a shift of treatment parts from inpatient to outpatient care found after German DRG introduction (Fürstenberg et al. (2011), Grobe et al. (2011)). Findings rely solely on data after DRG introduction.

• In Switzerland Busato and von Below (2010) look at the effect of hospital compensation on outpatient care costs per capita (descriptive statistics).

Empirical evidence on the relationship between inpatient reimbursement and the shift of treatment parts from inpatient to outpatient care contributes to the existing literature.



Theoretical framework Assumptions & utility function

• Patient visits his physician…

• Physician examines the patient and decides on treatment (how and where) - Patient acts accordingly

• Patient is fully insured and payments are directly made by the insurance company

• Treatments consist of inpatient care (i) and/or ambulatory care (a)

• For example, an inpatient surgical procedure and an outpatient CT scan equals 1000i and 100a.

Assumptions Health care provider utility

𝑢 𝑎, 𝑖 = 𝜋 𝑎, 𝑖 + 𝛾 ∙ 𝐵(𝑎, 𝑖)

Profit Patient benefit



Theoretical framework Components of care providers’ utility

Profit Patient benefit

𝜋 𝑎, 𝑖 = 𝑅 𝑎, 𝑖 − 𝑐 𝑎, 𝑖

𝑅 𝑎, 𝑖 = 𝑓𝑎 + 𝑣𝑎 ∙ 𝑎 + 𝑓𝑖 + 𝑣𝑖 ∙ 𝑖

Reimbursement

Cost of providing care

𝑓𝑎 , 𝑓𝑖: Fixed prospective component

𝑐 𝑎, 𝑖 = 𝑤𝑎 ∙ 𝑎 + 𝑤𝑖 ∙ 𝑖

𝑤𝑎 , 𝑤𝑖: Input prices per unit

𝑣𝑎 , 𝑣𝑖: Effective cost based component

• B 𝑎, 𝑖 increasing at a decreasing rate up to a maximum 𝑎𝑚𝑎𝑥 , 𝑖𝑚𝑎𝑥

• B 𝑎, 𝑖 is a strictly concave function with a unique maximum

• Benefit function is allowed to vary across patients/diseases

• Inpatient and outpatient care are

substitutes: 𝜕2𝐵 𝑎,𝑖

𝜕𝑎𝜕𝑖< 0



Theoretical framework Effect of inpatient reimbursement on outpatient care

First order conditions Comparative statics

• Derivative of the FOCs with respect to vi

• Solve for 𝜕a∗

𝜕vi and

𝜕i∗

𝜕vi

• Change in ambulatory care:

𝑢 𝑎, 𝑖 = 𝜋 𝑎, 𝑖 + 𝛾 ∙ 𝐵(𝑎, 𝑖)

(1) 0 = 𝑣𝑎 − 𝑤𝑎 + 𝛾 ∙𝜕𝐵

𝜕𝑎

(2) 0 = 𝑣𝑖 − 𝑤𝑖 + 𝛾 ∙𝜕𝐵

𝜕𝑖

𝜕𝑎∗

𝜕𝑣𝑖=1

𝛾∙

𝜕2𝐵 𝜕𝑎𝑖

𝜕2𝐵 𝜕𝑎𝑎 ∙ 𝜕2𝐵 𝜕𝑖𝑖 − 𝜕2𝐵 𝜕𝑎𝑖 2 < 0

Reduction in the proportional share of inpatient reimbursement causes an increase in ambulatory care



Swiss inpatient reimbursement Major change in 2012

AP-DRG PLT TP MIPP/ABP SDRG

2010 - 2011 Since 2012



Swiss inpatient reimbursement: Hypotheses Expected levels of outpatient care across Switzerland

AP-DRG

PLT

TP

SDRG

Note: MIPP/ABP is not included in the analysis as it applied only in 1 canton.

𝒗𝒊

• Effective cost sharing component 𝑣𝑖 drives the shift from inpatient to ambulatory care.

• Higher 𝑣𝑖 results in a lower level of ambulatory care.

• TP has the highest effective cost sharing component, AP-DRG / SDRG the lowest, and PLT is in between.

• Outpatient inputs in the production of patient health are lowest in TP regions, higher in PLT hospitals and highest in AP-DRG/SDRG regions.

Inpatient reimbursement Hypothesis






Index



Data sample Patient data of the largest Swiss health insurer

Data sample

• Patient level data of the largest Swiss health insurance company: Helsana AG

• Detailed information on all patients who had an inpatient treatment covered by a health insurance contract during any second quarter from 2010-2012.

Sample selection

• Canton of Aargau is excluded.

• Patient’s canton of residence has to match hospital location.

• Only inpatient stays in somatic care hospitals1 are considered.

• Only treatments that are covered by a mandatory health insurance contract.

• Top 1% of patients in terms of days in hospital are excluded.

1: K111, K112, K121, K122, K123, K231, K232 or K233 according to the BFS Krankenhaustypologie



Data sample overview 62’397 inpatient stays in 153 hospitals

Note: PLT: Prozess-Leistungs-Tarifierung; TP: Tagespauschale; SDRG: Swiss DRG

10'175 9'565

8'734 8'598

2'332 2'376

20'617

2010 2011 2012

Number of inpatient stays

AP-DRG PLT TP SDRG

Total 21’241 20’539 20’617

62 66

57 58

16 15

142

2010 2011 2012

Number of hospitals

AP-DRG PLT TP SDRG

139 135 142



Ambulatory care measurement Physician visits and taxpoints near an inpatient stay

Treatement

Pre-inpatient

ambulatory

care

Post-inpatient

ambulatory

care

Inpatient

stay

(hospital)

20 days before

hospitalization 20 days after

hospitalization

• Ambulatory physician visits (hospital outpatient facilities and physician offices)

• Billed taxpoints for ambulatory physician visits and laboratory analyses

• Outpatient care 20 days around an inpatient stay is considered inpatient stay linked (Alternatively 10, 5 days)

• Outpatient care more than 30 days away from an inpatient stay is considered unrelated.

Ambulatory care variables



Empirical strategy Introduction of Swiss DRG – a quasi experiment

• Nationwide DRG introduction in 2012

• SDRG introduction should not cause a major rise in ambulatory care in former AP-DRG regions

• Change in inpatient stay linked ambulatory care in other reimbursement systems (PLT, TP) compared to benchmark (AP-DRG)

Quasi-experiment like setting – difference-in-difference estimation

Comment Inpatient reimbursement



Methodology Difference-in-difference poisson regression

• Estimation method • Pooled poisson regression with cluster-robust standard errors

• Dependent variable • Ambulatory physician visits and billed taxpoints 20, 10, 5 days around an

inpatient stay

• Inpatient reimbursement variables

• Control variables • Patient level: inpatient stay related, socio-demographic, health, care usage • Hospital level: treated cases, hospital type

• Robustness checks • Regression set up • Estimation methodology






Index



Main Results Prospective payment causes a shift of treatment parts

Physician visits

Time window 5 days 10 days 20 days

PLT -0.116*** -0.0870*** -0.0835***

(0.0315) (0.0251) (0.0230)

TP -0.0752** -0.0751** -0.0629**

(0.0373) (0.0352) (0.0313)

PLT/SDRG 0.0841** 0.0928*** 0.106***

(0.0354) (0.0308) (0.0307)

TP/SDRG 0.0655** 0.0697** 0.0692**

(0.0317) (0.0289) (0.0281)

Taxpoints

Time window 5 days 10 days 20 days

PLT -0.157*** -0.130*** -0.109***

(0.0434) (0.0306) (0.0304)

TP -0.177*** -0.175*** -0.155***

(0.0452) (0.0382) (0.0355)

PLT/SDRG 0.0955** 0.101*** 0.111***

(0.0478) (0.0341) (0.0373)

TP/SDRG 0.107*** 0.0870*** 0.0942**

(0.0402) (0.0334) (0.0401)

Note: Robust standard errors in parentheses, *** p<0.01, ** p<0.05, * p<0.1



Robustness checks of the regression set up Main results are confirmed

Physician visits Taxpoints

Note: Used time window is 20 days around IS; Robust standard errors in parentheses, *** p<0.01, ** p<0.05, * p<0.1

(1) (2) (3) (4)

Standard Separate

time trends

Fixed effects

Not near IS

PLT -0.109*** -0.104*** -0.0552

(0.0304) (0.0304) (0.0430)

TP -0.155*** -0.152*** -0.0380

(0.0355) (0.0354) (0.0366)

PLT/SDRG 0.111*** 0.101*** 0.0934*** 0.0644

(0.0373) (0.0364) (0.0356) (0.0436)

TP/SDRG 0.0942** 0.0873** 0.0735** 0.0274

(0.0401) (0.0399) (0.0362) (0.0332)

(1) (2) (3) (4)

Standard Separate

time trends Fixed

effects Not near

IS

PLT -0.0835*** -0.0776*** 0.0202

(0.0230) (0.0226) (0.0294)

TP -0.0629** -0.0588* 0.0426

(0.0313) (0.0311) (0.0319)

PLT/SDRG 0.106*** 0.0946*** 0.0827*** 0.0537

(0.0307) (0.0299) (0.0295) (0.0372)

TP/SDRG 0.0692** 0.0609** 0.0447 0.00245

(0.0281) (0.0278) (0.0330) (0.0300)



Robustness checks of the estimation method Main results are confirmed

Physician visits

Note: Used time window is 20 days around IS; NB denotes negative-binomial regression; Robust standard errors in parentheses, *** p<0.01, ** p<0.05, * p<0.1

(1) (2) (3) (4) (5) (6)

Standard NB Zero Inflated Poisson Zero Inflated NB

Poisson Inflated NB Inflated

PLT -0.0835*** -0.0801*** -0.0688*** 0.144 -0.0603*** 0.518*

(0.0230) (0.0233) (0.0194) (0.183) (0.0190) (0.305)

TP -0.0629** -0.0550* -0.0710*** -0.128 -0.0662*** -0.352

(0.0313) (0.0318) (0.0258) (0.191) (0.0189) (0.324)

PLT/SDRG 0.106*** 0.0991*** 0.0854*** -0.214 0.0763*** -0.438

(0.0307) (0.0309) (0.0282) (0.245) (0.0259) (0.423)

TP/SDRG 0.0692** 0.0688** 0.0636*** -0.106 0.0699*** 0.239

(0.0281) (0.0280) (0.0237) (0.252) (0.0251) (0.430)



Conclusion & Summary

Theoretical prediction:

• Increase of outpatient inputs if the proportional share of reimbursement in inpatient care is reduced.

• Prospective hospital payment (DRG) is expected to increase the amount of ambulatory care related to inpatient stays.

Empirical findings:

• Empirical evidence supports theoretical prediction: Reduced share of proportional reimbursement increases ambulatory care linked to inpatient stays – shift of treatment parts.

• Patient data of a major Swiss Health insurance company.

• Ambulatory care is measured by the number of physician visits and billed taxpoints.

• Swiss DRG introduction offers a quasi-experiment.



THANK YOU!

A ny q u e st i o n s ?

→ [email protected]



B A C K U P



References

• Busato, A. and G. von Below (2010). The implementation of drg-based hospital reimbursement in Switzerland: A population-based perspective. Health Research Policy and Systems 8 (1), 31.

• Fürstenberg, T., M. Laschat, K. Zich, S. Klein, P. Gierling, H. Nolting, and T. Schmidt (2011). G-DRG-Begleitforschung gemäss § 17b Abs. 8 KHG. IGES Institut.

• Grobe, T., H. Dörning, and F. Schwartz (2011). BARMER GEK Arztreport 2011 – Auswertung zu Daten bis 2009. Schwäbisch Gmünd.

• Hadley, J., S. Zuckerman, and J. Feder (1989). Profits and fiscal pressure in the prospective payment system: Their impacts on hospitals. Inquiry 26, 354–65.

• Menke, T. (1990). Impacts of PPS on medicare part b expenditures and utilization for hospital episodes of care. Inquiry 27, 114–26.Sager, M., E. Leventhal, and D. Easterling (1987). The impact of medicare’s prospective payment system on wisconsin nursing homes. JAMA 257 (13), 1762–1766.

• Sager, M. A., D. V. Easterling, D. A. Kindig, and O. W. Anderson (1989). Changes in the location of death after passage of medicare’s prospective payment system. New England Journal of Medicine 320 (7), 433–439.

• Sloan, F. A. and J. Valvona (1986). Why has hospital length of stay declined? an evaluation of alternative theories. Social Science & Medicine 22 (1), 63 – 73.

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