Abstract
Mandatory measurement and disclosure of outcome measures are commonly used policy tools in healthcare. The effectiveness of such disclosures relies on the extent to which the new information produced by the mandatory system is internalized by the healthcare organization and influences its operations and decision-making processes. We use panel data from the Japanese National Hospital Organization to analyze performance improvements following regulation mandating standardized measurement and peer disclosure of patient satisfaction performance. Drawing on value of information theory, we document the absolute value and the benchmarking value of new information for future performance. Controlling for ceiling effects in the opportunities for improvement, we find that the new patient satisfaction measurement system introduced positive, significant, and persistent mean shifts in performance (absolute value of information) with larger improvements for poorly performing hospitals (benchmarking value of information). Our setting allows us to explore these effects in the absence of confounding factors such as incentive compensation or demand pressures. The largest positive effects occur in the initial period, and improvements diminish over time, especially for hospitals with poorer baseline performance. Our study provides empirical evidence that disclosure of patient satisfaction performance information has value to hospital decision makers.
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Data availability
Data used in this study can be obtained from the Japanese National Hospital Organization.
Notes
Yokota and Thompson (2004) provide a review of VOI models in healthcare.
Although VOI is sometimes interpreted rather narrowly as the amount a decision maker would be willing to pay for higher quality information, the analytical models of VOI are generic and refer to “value” in a flexible sense that allows for nonfinancial interpretations (Bromwich 2007; Demski 1972; Raiffa 1968).
This finding also reduces the concern that regression to the mean might be an alternative explanation for our findings.
Source: Guidebook of the National Hospital Organization—www.nho.hosp.go.jp.
Health Insurance in Japan is compulsory for all citizens and can be obtained either through the employer (Employees’ Health Insurance) or, in the case of self-employed individuals and students, through the National Health Insurance system. Special insurance programs are in place for elderly citizens (over 75 years). Patients pay about for 30% of the cost of medical services, with the remaining 70% being reimbursed to the hospital by the insurer or the government. Medical costs exceeding the equivalent of $600 in a month are fully reimbursed by the insurer or the government. Other than minor cost of living adjustments, these numbers have been steady since the year 2000.
The research team interviewed Dr. Kunio Nakai in October of 2017.
Physicians and medical staff at the NHO are compensated on a fixed wage basis and are not provided performance-contingent bonuses. Physicians and staff obtain raises based on general macro-economic conditions. Section 4 examines physician compensation at NHO hospitals in greater detail.
Although VOI is sometimes interpreted rather narrowly as the amount a decision maker would be willing to pay for higher quality information, analytical VOI models are generic and refer to “value” in a flexible sense that allows for non-pecuniary interpretations (Bromwich 2007; Demski 1972; Raiffa 1968).
Prior literature finds that in the absence of information, individuals and firms tend to hold optimistic beliefs about their ability and therefore are overconfident about their performance relative to competitors (Kahnemann et al. 1982).
National Hospital Organization (Independent Administrative Institution) page 1; http://www.mof.go.jp/english/filp/filp_report/zaito2004e-exv/24.pdf.
Source: Guidebook of the National Hospital Organization—www.nho.hosp.go.jp.
We do not have access to individual patient-level responses.
The surveys include sub-items for each of the 15 (19) questions. After validating that each group of sub-questions loaded on individual factors corresponding to the “header” question, we decided to focus on the 15 (19) header questions in order to ensure we would have sufficient statistical power for our analyses.
Items that cross-loaded on multiple factors were dropped (Ho 2013).
A prefecture is a geographical subdivision of the Japanese territory, conceptually equivalent to a county in the US.
Source: Guidebook of the National Hospital Organization—www.nho.hosp.go.jp.
A survey conducted by the Japanese Ministry of Health, Labor and Welfare during the period of the study explored the major drivers of hospital choice for inpatients and outpatients. The sample consisted of more than 150,000 respondents, randomly selected from the patient population of all Japanese Hospitals. Overall, outpatients (inpatients) identified the following drivers of hospital choice: 38% (34.9%) prior experience at the same hospital, 37.6% (29.9%) physical closeness to their residence, school or place of work, 33.2% (49%) recommendation by doctors, 31.4% (34.7%) kindness of doctors and nurses, and 28.7% (25.5%) size/technology of the hospital. Source: Japanese Ministry of Health, Labour and Welfare. (2011). Patients Behavior Survey, from http://www.mhlw.go.jp/english/new-info/2012.html.
Patient satisfaction with hospitals’ infrastructure is likely negatively impacted by aging buildings that had not been properly maintained during the pre-NHO era. Since 2004, the NHO has invested significant sums, mostly in the form of grants, to remodel and renovate its hospitals with a view to improve patient experience. However, because it is the policy of the NHO to balance their budget each year, and each hospital is responsible for breaking even, actual investments were slow to produce visible results. The disruption caused by renovation activities is likely to have caused the deterioration of patient satisfaction in some cases. Source: Guidebook of the National Hospital Organization—www.nho.hosp.go.jp.
While the distribution of the dependent variable is bounded above (below) by the value of the corresponding factor calculated for a hypothetical hospital that scores 5 (1) on all indicators relative to the factor, the construction of the factor variable is normalized by construction. Therefore, OLS is an appropriate estimator for this model.
To calculate the values corresponding to the maximum performance for each satisfaction factor, we computed each factor score for a hypothetical hospital scoring 5 on each question in the inpatient and outpatient questionnaires.
We would not be able to perform the test of H2 reported in Table 6 with hospital FE, since InitialPoorPerformer is a time-invariant characteristic that would be absorbed by the fixed effects. However, untabulated tests on two subsamples (respectively, poor initial performers and the rest of the population) yield consistent results.
Source: Guidebook of the National Hospital Organization—www.nho.hosp.go.jp.
To address the potential impact of variation in levels of pay on patient satisfaction improvements, we performed an additional analysis, in which we restricted the estimation of Eq. (8) to a subsample of hospital/year observations, constructed by identifying the hospitals in the highest quartile of average salary per staffed bed in each year and each type of hospital. The results (untabulated) of our estimation are consistent with those reported in Table 9.
Note that regression to the mean is primarily an issue when the analysis consists of only two observations, such as two variables measured on one occasion (e.g. control and treatment group in an experiment) or one variable measured on two occasions (e.g. pre-test post-test comparison after an experimental intervention). Regression to the mean is not a phenomenon that is relevant to multiple observations over time (Nesselroade et al. 1980).
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Acknowledgements
We thank for their valuable comments and suggestions Jeff Biddle, Clara Chen, Leslie Eldenburg, Regina Herzlinger, Bob Kaplan, Matthias Mahlendorf, Melissa Martin, Pam Murphy, Steve Salterio, Greg Sabin, Daniel Thornton, Stephanie Tsui, Jeff Wooldridge, workshop participants at the “Patient-Centric Healthcare Management in the Age of Analytics” conference, University of Arizona, Erasmus University, Michigan State University, Queen’s School of Business, and Wilfrid Laurier University. We appreciate the help we received in gathering and interpreting information about the Japanese healthcare industry from Nobuo Sato and Mayuka Yamazaki at the Harvard Business School Research Center in Tokyo, and for the precious insights on the institutional settings shared with us by Dr. Kunio Nakai, and by Kanoko Oishi. We thank Kenji Yasukata, Yoshinobu Shima and Chiyuki Kurisu for their support in the collection of the data used in this study, and to Sho Nihei for his translation services. All errors are our own.
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Appendices
Appendix 1: Survey instrument
Panel A: Overall satisfaction (Same questions asked separately for outpatients and inpatients; Scale 1 = Strongly Dissatisfied; 2 = Somewhat Dissatisfied; 3 = Neutral; 4 = Somewhat Satisfied; 5 = Strongly Satisfied
I am generally satisfied with this hospital |
I am satisfied with the results of the treatment |
I am satisfied with the period of the treatment |
I am satisfied with treatment I have been taking |
I am satisfied with the hospital |
I think this hospital provides safe medical services |
I think the explanations provided by the medical staff were very clear |
I think the treatment I have received was acceptable |
I generally trust this hospital |
I would like to recommend this hospital to family members and friends |
Panel B: Individualized questions for inpatients (Scale 1 = Strongly Agree; 2 = Somewhat Agree; 3 = Neither Agree nor Disagree; 4 = Somewhat Disagree; 5 = Strongly Disagree)
I am not satisfied with the explanation by doctors when I was hospitalized |
I was unhappy with the procedure of medical admission |
I was unhappy with hospital’s explanation about my life during the hospital stay |
I think that the doctors behave badly and use bad language in this hospital |
I was worried about some doctors’ skills and knowledge |
I think that the nurses behave badly and use bad language in this hospital |
I was unhappy with the assistance received for daily life activities |
I think that medical staff such as doctors, nurses and other medical staff lacked teamwork |
I did not like today’s medical tests (For patients who accepted medical tests) |
I did not like today’s medical surgeries (For patients who accepted medical surgeries) |
I did not like today’s medical treatment (For patients who accepted medical treatment) |
I did not like today’s drip, injection, medicine, or prescription (For patients who had a drip, injections, medicine, or prescription) |
I did not like today’s rehabilitation (For patients who had rehabilitation) |
I am unhappy with the toilets and bathrooms in this hospital |
I think that passageways, stairs and elevators are inconvenient |
I am unhappy with my room |
I am unhappy with the food in this hospital |
I am unhappy with the other environment such as stores, and interiors |
I am unhappy with the hospital’s explanation of my discharge |
Panel C: Individualized questions for outpatients (Scale 1 = Strongly Agree; 2 = Somewhat Agree; 3 = Neither Agree nor Disagree; 4 = Somewhat Disagree; 5 = Strongly Disagree)
I felt uneasy when I came to the hospital at the initial visit |
I think that this hospital is very inconvenient |
I have a bad impression about this hospital |
I am unhappy with waiting time |
I am unhappy with the waiting room |
I think that doctors behave badly and use bad language in this hospital |
I was worried about some doctors’ skills and knowledge |
I think that nurses behave badly and use bad language in this hospital |
I did not like today’s medical tests (For patients who accepted medical tests) |
I did not like today’s medical treatment (For patients who accepted medical treatment) |
I did not like today’s drip, injection, medicine, or prescription (For patients who had a drip, injections, medicine, or prescription) |
I did not like today’s rehabilitation (For patients who had rehabilitation) |
I am unhappy with the treatment room |
I am unhappy with the other environment such as shops, ATM, and interiors |
I am unhappy with the procedures for payment |
Appendix 2: Descriptive statistics for each question in the survey
Panel A: Inpatients
Inpatients | N | Mean | SD | p25 | p50 | p75 | Min | Max |
|---|---|---|---|---|---|---|---|---|
Doctors explanations when hospitalized | 1126 | 4.332 | 0.411 | 4.285 | 4.438 | 4.543 | 1.000 | 5.000 |
Admission procedures | 1128 | 4.301 | 0.392 | 4.248 | 4.393 | 4.500 | 1.000 | 5.000 |
Explanation about life during hospital stay | 1128 | 4.137 | 0.373 | 4.014 | 4.196 | 4.333 | 2.000 | 5.000 |
Doctors’ behavior | 1128 | 4.522 | 0.306 | 4.473 | 4.585 | 4.678 | 2.000 | 5.000 |
Doctors’ skills | 1127 | 4.478 | 0.324 | 4.419 | 4.548 | 4.645 | 2.000 | 5.000 |
Nurses’ behavior | 1126 | 4.381 | 0.388 | 4.333 | 4.481 | 4.580 | 2.000 | 5.000 |
Assistance for daily life | 1126 | 4.453 | 0.347 | 4.382 | 4.536 | 4.632 | 2.000 | 5.000 |
Clinician teamwork | 1126 | 4.399 | 0.353 | 4.336 | 4.474 | 4.580 | 1.714 | 5.000 |
Medical tests | 1120 | 4.518 | 0.323 | 4.468 | 4.578 | 4.681 | 2.000 | 5.000 |
Medical surgeries | 1051 | 4.571 | 0.430 | 4.543 | 4.683 | 4.776 | 1.000 | 5.000 |
Medical treatment | 1114 | 4.575 | 0.351 | 4.546 | 4.657 | 4.745 | 2.000 | 5.000 |
Drip, injection, medicine, prescription | 1118 | 4.493 | 0.401 | 4.440 | 4.578 | 4.676 | 1.000 | 5.000 |
Rehabilitation | 949 | 4.379 | 0.417 | 4.250 | 4.441 | 4.593 | 1.000 | 5.000 |
Toilets and bathrooms | 1125 | 4.019 | 0.476 | 3.746 | 4.049 | 4.357 | 1.000 | 5.000 |
Passageways, stairs, elevators | 1123 | 4.272 | 0.376 | 4.098 | 4.329 | 4.509 | 1.000 | 5.000 |
My room | 1125 | 4.072 | 0.448 | 3.818 | 4.108 | 4.398 | 1.500 | 5.000 |
Food | 1124 | 4.040 | 0.399 | 3.885 | 4.092 | 4.268 | 2.000 | 5.000 |
Stores and interiors | 1123 | 4.025 | 0.424 | 3.859 | 4.084 | 4.287 | 1.000 | 5.000 |
Explanations at discharge | 1126 | 4.316 | 0.311 | 4.200 | 4.360 | 4.479 | 2.556 | 5.000 |
Overall satisfaction—inpatients | N | Mean | SD | p25 | P50 | P75 | Min | Max |
|---|---|---|---|---|---|---|---|---|
Generally satisfied | 1130 | 4.317 | 0.360 | 4.223 | 4.388 | 4.520 | 1.000 | 5.000 |
Results of the treatment | 1130 | 4.335 | 0.360 | 4.226 | 4.419 | 4.528 | 1.000 | 5.000 |
Length of the treatment | 1129 | 4.233 | 0.384 | 4.152 | 4.318 | 4.433 | 1.000 | 5.000 |
Treatment | 1130 | 4.381 | 0.356 | 4.326 | 4.460 | 4.559 | 1.000 | 5.000 |
Hospital | 1129 | 4.233 | 0.353 | 4.155 | 4.298 | 4.406 | 1.000 | 5.000 |
Safety of medical services | 1130 | 4.453 | 0.345 | 4.360 | 4.538 | 4.637 | 1.000 | 5.000 |
Clear explanations | 991 | 4.492 | 0.314 | 4.440 | 4.549 | 4.635 | 1.000 | 5.000 |
Treatment was acceptable | 991 | 4.467 | 0.356 | 4.416 | 4.555 | 4.644 | 1.000 | 5.000 |
Trust | 991 | 4.534 | 0.321 | 4.486 | 4.600 | 4.689 | 1.000 | 5.000 |
Recommend to family and friends | 1081 | 4.359 | 0.439 | 4.305 | 4.459 | 4.577 | 1.000 | 5.000 |
Panel B: Outpatients
Outpatients | N | Mean | SD | P25 | P50 | P75 | Min | Max |
|---|---|---|---|---|---|---|---|---|
Felt uneasy | 1149 | 3.695 | 0.255 | 3.582 | 3.716 | 3.839 | 2.667 | 5.000 |
Inconvenient | 1149 | 3.683 | 0.386 | 3.454 | 3.746 | 3.955 | 2.043 | 4.857 |
Bad impression | 1149 | 4.051 | 0.333 | 3.938 | 4.114 | 4.250 | 1.667 | 5.000 |
Waiting time | 1150 | 3.116 | 0.374 | 2.841 | 3.055 | 3.337 | 2.279 | 5.000 |
Waiting room | 1150 | 3.789 | 0.318 | 3.583 | 3.818 | 4.000 | 2.688 | 5.000 |
Doctors’ behavior | 1150 | 4.165 | 0.221 | 4.018 | 4.155 | 4.318 | 3.333 | 5.000 |
Doctors’ skills | 1150 | 4.075 | 0.243 | 3.920 | 4.068 | 4.230 | 2.667 | 5.000 |
Nurses’ behavior | 1150 | 4.101 | 0.235 | 3.963 | 4.100 | 4.242 | 2.563 | 5.000 |
Medical tests | 1149 | 4.108 | 0.248 | 3.964 | 4.119 | 4.273 | 2.667 | 5.000 |
Medical treatment | 1149 | 4.303 | 0.235 | 4.182 | 4.325 | 4.455 | 3.000 | 5.000 |
Drip, injection, medicine, prescription | 1149 | 4.299 | 0.253 | 4.156 | 4.319 | 4.467 | 2.750 | 5.000 |
Rehabilitation | 1002 | 4.093 | 0.350 | 3.898 | 4.086 | 4.290 | 1.000 | 5.000 |
Treatment room | 1150 | 4.141 | 0.269 | 3.982 | 4.157 | 4.326 | 1.000 | 5.000 |
Shops, ATM, interiors | 1149 | 3.833 | 0.312 | 3.629 | 3.848 | 4.042 | 1.000 | 5.000 |
Procedures for payment | 1150 | 3.859 | 0.327 | 3.670 | 3.865 | 4.070 | 1.000 | 5.000 |
Overall satisfaction—outpatients | N | Mean | SD | P25 | P50 | P75 | Min | Max |
|---|---|---|---|---|---|---|---|---|
Generally satisfied | 1150 | 4.071 | 0.213 | 3.930 | 4.080 | 4.208 | 2.667 | 5.000 |
Results of the treatment | 1150 | 4.026 | 0.217 | 3.891 | 4.025 | 4.161 | 2.667 | 5.000 |
Length of the treatment | 1150 | 3.921 | 0.222 | 3.789 | 3.911 | 4.057 | 2.333 | 5.000 |
Treatment | 1150 | 4.032 | 0.223 | 3.895 | 4.016 | 4.169 | 2.333 | 5.000 |
Hospital | 1150 | 3.952 | 0.217 | 3.817 | 3.938 | 4.088 | 2.333 | 5.000 |
Safety of medical services | 1150 | 4.156 | 0.204 | 4.026 | 4.157 | 4.289 | 2.667 | 5.000 |
Clear explanations | 1150 | 4.174 | 0.216 | 4.052 | 4.172 | 4.311 | 2.000 | 5.000 |
Treatment was acceptable | 1005 | 4.157 | 0.211 | 4.038 | 4.163 | 4.294 | 3.158 | 5.000 |
Trust | 1005 | 4.266 | 0.197 | 4.150 | 4.276 | 4.396 | 3.000 | 5.000 |
Recommend to family and friends | 1150 | 4.077 | 0.252 | 3.938 | 4.086 | 4.236 | 2.000 | 5.000 |
Appendix 3: Variables definition
Hospital characteristics | |
|---|---|
Size | Number of beds available in the hospital, expressed in hundreds (i.e., number of beds/100) |
Concentration | Number of hospitals (NHO and not) per 100 thousand inhabitants in the prefecture |
Hospital | Indicator variable coded as 1 if the hospital is a general hospital and coded as 0 if the hospital is a sanatorium |
Salary expenses (¥B) | Expenses due to salary compensation (billion Yen) |
Bonus expenses (¥B) | Expenses due to bonus compensation (billion Yen) |
Grant revenues (¥B) | Grant revenues received by the hospital (billion Yen) |
Medical revenues (¥B) | Expenses related to medical services provided by the hospital (billion Yen) |
Education revenues (¥B) | Expenses related to teaching (medical school, nursing school) (billion Yen) |
R&D revenues (¥B) | Expenses related to clinical and academic research (billion Yen) |
Other costs (¥B) | Total medical costs other than the categories identified above (billion Yen) |
Appendix 4: Physician compensation at NHO
Salary schedule Each NHO post is classified into a certain grade in a salary schedule. The classification of the employee into a post is based on two factors: educational classification and experience. Most Japanese government agencies have ten grades. Within each grade employees receive raises in steps, which are based on time in grade. A sample of the pay scale for a Japanese government agency is provided below.
Salary per month (Yen) | |||||||||||
|---|---|---|---|---|---|---|---|---|---|---|---|
Grade | |||||||||||
1 | 2 | 3 | 4 | 5 | 6 | 7 | 8 | 9 | 10 | ||
Steps | 1 | 135,600 | 185,800 | 222,900 | 261,900 | 289,200 | 320,600 | 366,200 | 413,000 | 466,700 | 532,000 |
5 | 140,100 | 192,800 | 230,200 | 270,200 | 298,200 | 329,800 | 376,300 | 422,800 | 479,000 | 544,700 | |
9 | 144,500 | 200,000 | 237,500 | 278,600 | 307,300 | 338,600 | 386,400 | 432,300 | 491,300 | 554,700 | |
13 | 149,800 | 207,000 | 244,900 | 287,000 | 316,400 | 347,200 | 397,100 | 441,300 | 503,600 | 562,100 | |
17 | 155,700 | 214,600 | 252,200 | 295,400 | 325,200 | 355,500 | 406,400 | 449,300 | 513,300 | 568,100 | |
21 | 161,600 | 222,000 | 260,100 | 303,800 | 333,500 | 363,500 | 414,800 | 456,500 | 519,000 | 572,900 | |
25 | 172,200 | 229,300 | 267,700 | 312,100 | 341,500 | 371,500 | 422,900 | 462,500 | 524,800 | ||
29 | 178,800 | 236,100 | 275,300 | 320,400 | 349,400 | 379,500 | 429,400 | 467,800 | 529,600 | ||
33 | 185,800 | 242,100 | 282,700 | 328,400 | 357,000 | 386,900 | 434,600 | 471,000 | 533,100 | ||
37 | 191,600 | 248,000 | 290,100 | 336,500 | 364,200 | 393,700 | 439,700 | 474,200 | 536,700 | ||
41 | 196,900 | 254,200 | 297,400 | 344,400 | 370,100 | 398,400 | 443,200 | 477,400 | 540,300 | ||
45 | 202,000 | 259,700 | 304,200 | 352,000 | 374,700 | 403,000 | 446,400 | 480,500 | |||
49 | 207,100 | 265,200 | 310,600 | 358,500 | 378,400 | 405,900 | 449,400 | ||||
53 | 211,600 | 270,100 | 317,100 | 363,000 | 381,700 | 408,800 | 452,400 | ||||
57 | 215,400 | 275,200 | 323,400 | 367,100 | 384,500 | 411,600 | 455,400 | ||||
61 | 219,200 | 279,700 | 328,100 | 369,800 | 387,000 | 414,300 | 458,400 | ||||
65 | 223,000 | 283,500 | 331,900 | 372,400 | 389,600 | 416,900 | |||||
69 | 226,900 | 287,200 | 335,200 | 375,000 | 392,200 | 419,400 | |||||
73 | 230,100 | 290,400 | 337,800 | 377,600 | 394,800 | 422,000 | |||||
77 | 233,000 | 292,300 | 340,000 | 380,200 | 397,300 | 424,600 | |||||
81 | 236,100 | 293,800 | 342,000 | 382,700 | 399,900 | ||||||
85 | 239,000 | 295,300 | 344,000 | 385,100 | 402,500 | ||||||
89 | 241,900 | 296,800 | 345,900 | 387,600 | |||||||
93 | 243,700 | 298,200 | 347,700 | 390,100 | |||||||
97 | 299,600 | 349,500 | |||||||||
Composition of salary In addition to the monthly salary, government employees also get allowances averaging at about 20% of base salary. The allowances include: living expenses (cost of living adjustment), housing allowance, commuter allowance, overtime allowance, cold weather allowance, and diligence allowance (typically based on the number of months of consecutive work in the previous 6-month period). There are some compensation adjustments related to macroeconomic conditions. Individual performance-based bonuses are not commonly found.
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Gallani, S., Kajiwara, T. & Krishnan, R. Value of new performance information in healthcare: evidence from Japan. Int J Health Econ Manag. 20, 319–357 (2020). https://doi.org/10.1007/s10754-020-09283-1
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DOI: https://doi.org/10.1007/s10754-020-09283-1