Original Research

A Multi-Membership Approach for Attributing Patient-Level Outcomes to Providers in an Inpatient Setting

Journal of Clinical Outcomes Management. 2021 January;28(1):17-26 | 10.12788/jcom.0036

References

1. Abduljawad A, Al-Assaf AF. Incentives for better performance in health care. Sultan Qaboos Univ Med J. 2011;11:201-206.

2. Milstein R, Schreyoegg J. Pay for performance in the inpatient sector: a review of 34 P4P programs in 14 OECD countries. Health Policy. 2016;120:1125-1140.

3. Herzke CA, Michtalik HJ, Durkin N, et al. A method for attributing patient-level metrics to rotating providers in an inpatient setting. J Hosp Med. 2018;13:470-475.

4. Batbaatar E, Dorjdagva J, Luvsannyam A, Savino MM, Amenta P. Determinants of patient satisfaction: a systematic review. Perspect Public Health. 2017;137:89-101.

5. Ballou D, Sanders W, Wright P. Controlling for student background in value-added assessment of teachers. J Educ Behav Stat. 2004;29:37-65.

6. Hill PW, Goldstein H. Multilevel modeling of educational data with cross-classification and missing identification for units. J Educ Behav Stat. 1998;23:117-128.

7. Rasbash J, Browne WJ. Handbook of Multilevel Analysis. Springer; 2007.

8. Brown WJ, Goldstein H, Rasbash J. Multiple membership multiple classification (MMMC) models. Statistical Modeling. 2001;1:103-124.

9. Sanders WL, Horn SP. The Tennessee Value-Added Assessment System (TVAAS)—mixed-model methodology in educational assessment. J Pers Eval Educ. 1994;8:299-311.

10. Kroch EA, Duan M. CareScience Risk Assessment Model: Hospital Performance Measurement. Premier, Inc., 2008. http://www.ahrq.gov/qual/mortality/KrochRisk.htm

11. Schumacher DJ, Wu DTY, Meganathan K, et al. A feasibility study to attribute patients to primary interns on inpatient ward teams using electronic health record data. Acad Med. 2019;94:1376-1383.

12. Simoes J, Krumholz HM, Lin Z. Hospital-level 30-day risk-standardized readmission measure. Centers for Medicare & Medicaid Services, 2018. https://www.cms.gov/Medicare/Quality-Initiatives-Patient-Assessment-Instruments/HospitalQualityInits/Downloads/Hospital-Wide-All-Cause-Readmission-Updates.zip

13. Krol MW, de Boer D, Delnoij DM, Rademakers JJDJM. The Net Promoter Score: an asset to patient experience surveys? Health Expect. 2015;18:3099-3109.

14. Doyle C, Lennox L, Bell D. A systematic review of evidence on the links between patient experience and clinical safety and effectiveness. BMJ Open. 2013;3:e001570.

15. Henderson CR. Sire evaluation and genetic trends. J Anim Sci. 1973;1973:10-41.

16. Mood AM. Introduction to the Theory of Statistics. McGraw-Hill; 1950:xiii, 433-xiii.

17. Rao JNK, Molina I. Small Area Estimation. Wiley; 2015.

Individual Provider Metrics for the PAPR Method

Inpatient LOS Index. Hospital inpatient LOS was measured as the number of days between admission date and discharge date. For each hospital visit, an expected LOS was determined using Premier’s CareScience Analytics (CSA) risk-adjustment methodology.¹⁰ The CSA methodology for LOS incorporates a patient’s clinical history, demographics, and visit-related administrative information.

Let n_j be the number of hospitalizations attributed to provider j. Let o_i_j and e_i_j be the observed and expected LOS, respectively, for hospitalization i = 1,…,n_j attributed to provider j. Then the inpatient LOS index for provider j is L_j= ∑_io_i_j⁄∑_ie_ij.

Inpatient Mortality Index. Inpatient mortality was defined as the death of the patient during hospitalization. For each hospitalization, an expected mortality probability was determined using Premier’s CSA risk-adjustment methodology.¹⁰The CSA methodology for mortality incorporates a patient’s demographics and comorbidities.

Just as before, let n_j be the number of hospitalizations attributed to provider j. Let m_ij = 1 if the patient died during hospitalization i = 1, … , n_j attributed to provider j; m_ij= 0 otherwise. Let p_ij^(m) be the corresponding expected mortality probability. Then the inpatient mortality index for provider j is M_j = ∑_im_i_j⁄∑_ip_ij^(m).

30-Day Inpatient Readmission Index. A 30-day inpatient readmission was defined as the event when a patient is discharged and readmits back into the inpatient setting within 30 days. The inclusion criteria defined by the Centers for Medicare and Medicaid Services (CMS) all-cause hospital-wide readmission measure was used and, consequently, planned readmissions were excluded.¹² Readmissions could occur at any Banner hospital, including the same hospital. For each hospital visit, an expected readmission probability was derived using Premier’s CSA risk-adjustment methodology.¹⁰ The CSA methodology for readmissions incorporates a patient’s clinical history, demographics, and visit-related administrative information.

Let n_j be the number of hospitalizations attributed to provider j. Let r_ij = 1 if the patient had a readmission following hospitalization i = 1, … , n_jattributed to provider j; r_ij = 0 otherwise. Let p_ij^(r) be the corresponding expected readmission probability. Then the 30-day inpatient readmission index for provider j is R_j = ∑_ir_ij ⁄∑_ip_ij^(r).

Patient Survey Scores. The satisfaction of the patient’s experience during hospitalization was measured via post-discharge surveys administered by InMoment. Two survey questions were selected because they related directly to a provider’s interaction with the patient: “My interactions with doctors were excellent” (Doctor) and “I received the best possible care” (Care). A third question, “I would recommend this hospital to my family and friends,” was selected as a proxy measure of the overall experience and, in the aggregate, is referred to as the net promoter score (NPS).^13,14 The responses were measured on an 11-point Likert scale, ranging from “Strongly Disagree” (0) to “Strongly Agree” (10); “N/A” or missing responses were excluded.

The Likert responses were coded to 3 discrete values as follows: if the value was between 0 and 6, then -1 (ie, detractor); between 7 and 8 (ie, neutral), then 0; otherwise 1 (ie, promoter). Averaging these coded responses results in a patient survey score for each question. Specifically, let n_j be the number of hospitalizations attributed to provider j in which the patient responded to the survey question. Let s_ij ∈{−1, 0, 1} be the coded response linked to hospitalization i = 1, … , n_j attributed to provider j. Then the patient experience score for provider j is S_j = ∑_is_i_j⁄n_j.

Handling Ties in Provider Performance Measures. Because ties can occur in the PAPR approach for all measures, a tie-breaking strategy is needed. For LOS indices, ties are less likely because their numerator is strictly greater than 0, and expected LOS values are typically distinct enough. Indeed, no ties were found in this study for LOS indices. However, mortality and readmission indices can routinely result in ties when the best possible index is achieved, such as 0 deaths or readmissions among attributed hospitalizations. To help differentiate between those indices in the PAPR approach, the total estimated risk (denominator) was utilized as a secondary scoring criterion.

Mortality and readmission metrics were addressed by sorting first by the outcome (mortality index), and second by the denominator (total estimated risk). For example, if provider A has the same mortality rate as provider B, then provider A would be ranked higher if the denominator was larger, indicating a higher risk for mortality.

Similarly, it was very common for providers to have the same overall average rating for a survey question. Therefore, the denominator (number of respondents) was used to break ties. However, the denominator sorting was bidirectional. For example, if the tied score was positive (more promoters than detractors) for providers A and B, then provider A would be ranked higher if the denominator was larger. Conversely, if the tied score between providers A and B was neutral or negative (more detractors than promoters), then provider A would be ranked lower if the denominator was larger.