Missing the Mark: Inaccuracy of administrative data in identification of hospitalized patients with pneumonia and results of a systematic clinical reclassification process on readmission rates

Objective: Pneumonia readmissions carry financial ramifications under the Hospital Readmissions Reduction Program (HRRP). As readmission determination utilizes administrative data, healthcare systems should evaluate accuracy of pneumonia diagnoses. We sought to develop a systemic process for pneumonia classification review and determine potential effects on pneumonia readmissions in a tertiary academic medical center in the United States.Methods: We performed independent reviews of all pneumonia discharges within 48 hours of discharge over a one-year period. We reclassified all pneumonia discharges into four categories based on the Centers for Disease Control and Prevention reference standard. Secondary review of discordant classifications was performed by discharging providers to determine final diagnosis. The primary outcome was readmission rate within 30 days by pneumonia clinical classification category.Results: Two hundred seventy-eight discharges were reviewed, with overall readmission rate of 18.0%. Independent review confirmed 191 cases (68.7%) as definite or probable pneumonia, while 87 cases (31.3%) were classified as either probably not or not pneumonia. Readmission rates differed significantly between cases reviewed as pneumonia vs. those reviewed as unlikely to be pneumonia (14.1% vs. 26.4%, p < .02). Discharging attending physicians agreed with independent reviewers in 58/87 cases (66.6%), attenuating readmission differences (rate 16.8% for those finalized as pneumonia vs. 22.4% for another diagnosis, p = .32). Pneumonia readmissions were reduced by 1.2% using the classification standard.Conclusions: Complex conditions such as pneumonia may be inaccurately diagnosed in many patients, potentially affecting penalties associated with readmission rates. Therefore, it is imperative that healthcare systems adopt systematic review processes to standardize diagnoses and improve comparative administrative data.

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Pharmacist Transition-of-Care Services Improve Patient Satisfaction and Decrease Hospital Readmissions

Journal of Pharmacy Practice ◽

10.1177/0897190020958264 ◽

2020 ◽

pp. 089719002095826

Author(s):

Katherine L. March ◽

Michael J. Peters ◽

Christopher K. Finch ◽

Lauchland A. Roberts ◽

Katie M. McLean ◽

...

Keyword(s):

Patient Satisfaction ◽

Medical Center ◽

Academic Medical Center ◽

Hospital Readmissions ◽

Transitions Of Care ◽

Control Group ◽

Readmission Rates ◽

Improve Patient Satisfaction ◽

Phone Calls ◽

Improve Patient

Background: Pharmacists ability to directly impact patient satisfaction through increases in the Hospital Consumer Assessment of Healthcare Providers and Systems (HCAHPS) surveys utilizing transitions-of-care (TOC) services is unclear. Methods: Retrospective analysis of TOC patients from 07/01/2018 to 03/31/2019 was conducted. Intervention (INV) patients received pharmacist medication reconciliation and education prior to discharge and post-discharge telephone follow-up. All other patients served as control group (CON). Primary outcome: Evaluate impact of TOC services on HCAHPS scores for “Communication about Medicines” and “Care Transitions.” Secondary outcomes: 30-day readmissions, quantification of prevented potential safety events, assessment of discharge prescriptions sent to the academic medical center outpatient pharmacy (MOP) for TOC patients. Results: Of 1,728 patients screened, 414 patients met inclusion criteria (INV = 414, CON = 1314). A significant improvement (14.7%; p = <0.0001) in overall medication-related HCAHPS results was seen when comparing pre- vs post-implementation of the TOC service. Statistically significant increases for individual questions “staff told you what the medicine was for” (14.2%; p = 0.018), “staff describe possible effects” (21.2%; p = 0.004), and “understood the purpose of taking medications” (11.4%; p = 0.035) were observed. A non-significant decrease in 30-day readmission rates for the groups was observed (CON 16.4%, INV 13.3%; p = 0.133); however, an unplanned subgroup analysis evaluating impact of discharge phone calls on 30-day readmission rates revealed a significant reduction of 17.3% to 12.4% (p = 0.007). One hundred forty-three medication safety event(s) were potentially prevented by the TOC pharmacist. Lastly, 562 prescriptions were captured at the MOP as a result of the TOC initiative. Conclusions: Pharmacy-based TOC models can improve patient satisfaction, prevent hospital readmissions, and generate revenue.

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Impacting Readmission Rates and Patient Satisfaction: Results of a Discharge Pharmacist Pilot Program

Hospital Pharmacy ◽

10.1310/hpj4611-876 ◽

2011 ◽

Vol 46 (11) ◽

pp. 876-883 ◽

Cited By ~ 19

Author(s):

Samaneh Tavalali Wilkinson ◽

Pal Aroop ◽

J. Couldry Richard

Keyword(s):

Patient Satisfaction ◽

High Risk ◽

Readmission Rate ◽

Medical Center ◽

Academic Medical Center ◽

Control Group ◽

Unplanned Readmission ◽

Discharge Process ◽

Readmission Rates ◽

The Impact

Background Readmission to a hospital within 30 days of discharge has become a key quality outcome measure. With an observed 30-day readmission rate as high as 20% and attributed costs of almost $17.4 billion a year for Medicare patients, the potential implications for patients and the entire health care system are significant. Medication-related complications have been shown to increase the risk for unplanned readmission. Pharmacists have an opportunity to impact quality and cost by risk stratifying and identify patients at high risk for hospital readmission. Objective To study the impact of a pharmacist-driven discharge counseling program for high-risk patients identified by BOOST (Better Outcomes for Older adults through Safe Transitions) criteria on 30-day readmission rates. Method This was a prospective, cohort, nonrandomized trial at a single medical-surgical unit with telemetry capability at a single academic medical center including 669 patients who were older than 18 years. Primary outcome was 30-day readmission rate. Secondary outcomes were the number and type of pharmacist interventions, cost avoidance, and patient satisfaction results. Results The readmission rate for patients counseled by a pharmacist during the discharge process was 15.7% compared to 21.6% for patients not counseled by a pharmacist on discharge (relative risk [RR] 0.728; 95% confidence interval [CI], 0.514–1.032; P = .04). The readmission rate for adult medicine patients not counseled at discharge by a pharmacist in the study was comparable to the readmission rates of a similar patient control group at 3 months and 1 year prior to the initiation of the study (18.7% and 19.1% vs 19.6%). Conclusions Pharmacists' support in the discharge process facilitated increased communication on the multidisciplinary team and resulted in a lower unplanned readmission rate for patients.

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Pitfalls of calculating hospital readmission rates based on nonvalidated administrative data sets

Journal of Neurosurgery Spine ◽

10.3171/2012.10.spine12559 ◽

2013 ◽

Vol 18 (2) ◽

pp. 134-138 ◽

Cited By ~ 44

Author(s):

Beejal Y. Amin ◽

Tsung-Hsi Tu ◽

William W. Schairer ◽

Lumine Na ◽

Steven Takemoto ◽

...

Keyword(s):

Direct Cost ◽

Administrative Data ◽

Spine Surgery ◽

Readmission Rate ◽

Chart Review ◽

Medical Center ◽

Data Sets ◽

Readmission Rates ◽

Staged Surgery ◽

The University

Object Administrative databases are increasingly being used to establish benchmarks for quality of care and to compare performance across peer hospitals. As proposals for accountable care organizations are being developed, readmission rates will be increasingly scrutinized. The purpose of the present study was to assess whether the all-cause readmissions rate appropriately reflects the University of California, San Francisco (UCSF) Medical Center hospital's clinically relevant readmission rate for spine surgery patients and to identify predictors of readmission. Methods Data for 5780 consecutive patient encounters managed by 10 spine surgeons at UCSF Medical Center from October 2007 to June 2011 were abstracted from the University HealthSystem Consortium (UHC) using the Clinical Data Base/Resource Manager. Of these 5780 patient encounters, 281 patients (4.9%) were rehospitalized within 30 days of the previous discharge date. The authors performed an independent chart review to determine clinically relevant reasons for readmission and extracted hospital administrative data to calculate direct costs. Univariate logistic regression analysis was used to evaluate possible predictors of readmission. The two-sample t-test was used to examine the difference in direct cost between readmission and nonreadmission cases. Results The main reasons for readmission were infection (39.8%), nonoperative management (13.4%), and planned staged surgery (12.4%). The current all-cause readmission algorithm resulted in an artificially high readmission rate from the clinician's point of view. Based on the authors' manual chart review, 69 cases (25% of the 281 total readmissions) should be excluded because 39 cases (13.9%) were planned staged procedures; 16 cases (5.7%) were unrelated to spine surgery; and 14 surgical cases (5.0%) were cancelled or rescheduled at index admission due to unpredictable reasons. When these 69 cases are excluded, the direct cost of readmission is reduced by 29%. The cost variance is in excess of $3 million. Predictors of readmission were admission status (p < 0.0001), length of stay (p = 0.0001), risk of death (p < 0.0001), and age (p = 0.021). Conclusions The authors' findings identify the potential pitfalls in the calculation of readmission rates from administrative data sets. Benchmarking algorithms for defining hospitals' readmission rates must take into account planned staged surgery and eliminate unrelated reasons for readmission. When this is implemented in the calculation method, the readmission rate will be more accurate. Current tools overestimate the clinically relevant readmission rate and cost.

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Common sampling and modeling approaches to analyzing readmission risk that ignore clustering produce misleading results

10.21203/rs.3.rs-27550/v1 ◽

2020 ◽

Author(s):

Huaqing Zhao ◽

Samuel Tanner ◽

Sherita H. Golden ◽

Susan Fisher ◽

Daniel J. Rubin

Keyword(s):

Logistic Regression ◽

Readmission Rate ◽

Medical Center ◽

Academic Medical Center ◽

Model Performance ◽

Readmission Rates ◽

Modeling Approaches ◽

Multiple Variables ◽

Highly Correlated ◽

Readmission Risk

Abstract Background There is little consensus on how to sample hospitalizations and analyze multiple variables to model readmission risk. The purpose of this study was to compare readmission rates and the accuracy of predictive models based on different sampling and multivariable modeling approaches. Methods We conducted a retrospective cohort study of 17 284 adult diabetes patients with 44 203 discharges from an urban academic medical center between 1/1/2004 and 12/31/2012. Models for all-cause 30-day readmission were developed by four strategies: logistic regression using the first discharge per patient (LR-first), logistic regression using all discharges (LR-all), generalized estimating equations (GEE) using all discharges, and cluster-weighted (CW) GEE using all discharges. Multiple sets of models were developed and internally validated across a range of sample sizes. Results The readmission rate was 10.2% among first discharges and 20.3% among all discharges, revealing that sampling only first discharges underestimates a population’s readmission rate. Number of discharges was highly correlated with number of readmissions (r = 0.87, P < 0.001). Accounting for clustering with GEE and CWGEE yielded more conservative estimates of model performance than LR-all. LR-first produced falsely optimistic Brier scores. Model performance was unstable below samples of 6 000–8 000 discharges and stable in larger samples. GEE and CWGEE performed better in larger samples than in smaller samples. Conclusions Hospital readmission risk models should be based on all discharges as opposed to just the first discharge per patient and utilize methods that account for clustered data.

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Common sampling and modeling approaches to analyzing readmission risk that ignore clustering produce misleading results

10.21203/rs.3.rs-27550/v2 ◽

2020 ◽

Author(s):

Huaqing Zhao ◽

Samuel Tanner ◽

Sherita H. Golden ◽

Susan Fisher ◽

Daniel J. Rubin

Keyword(s):

Logistic Regression ◽

Readmission Rate ◽

Medical Center ◽

Academic Medical Center ◽

Model Performance ◽

Readmission Rates ◽

Modeling Approaches ◽

Multiple Variables ◽

Highly Correlated ◽

Readmission Risk

Abstract Background: There is little consensus on how to sample hospitalizations and analyze multiple variables to model readmission risk. The purpose of this study was to compare readmission rates and the accuracy of predictive models based on different sampling and multivariable modeling approaches. Methods: We conducted a retrospective cohort study of 17 284 adult diabetes patients with 44 203 discharges from an urban academic medical center between 1/1/2004 and 12/31/2012. Models for all-cause 30-day readmission were developed by four strategies: logistic regression using the first discharge per patient (LR-first), logistic regression using all discharges (LR-all), generalized estimating equations (GEE) using all discharges, and cluster-weighted (CW) GEE using all discharges. Multiple sets of models were developed and internally validated across a range of sample sizes. Results: The readmission rate was 10.2% among first discharges and 20.3% among all discharges, revealing that sampling only first discharges underestimates a population’s readmission rate. Number of discharges was highly correlated with number of readmissions (r=0.87, P<0.001). Accounting for clustering with GEE and CWGEE yielded more conservative estimates of model performance than LR-all. LR-first produced falsely optimistic Brier scores. Model performance was unstable below samples of 6 000-8 000 discharges and stable in larger samples. GEE and CWGEE performed better in larger samples than in smaller samples. Conclusions: Hospital readmission risk models should be based on all discharges as opposed to just the first discharge per patient and utilize methods that account for clustered data.

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Common sampling and modeling approaches to analyzing readmission risk that ignore clustering produce misleading results

BMC Medical Research Methodology ◽

10.1186/s12874-020-01162-0 ◽

2020 ◽

Vol 20 (1) ◽

Author(s):

Huaqing Zhao ◽

Samuel Tanner ◽

Sherita H. Golden ◽

Susan G. Fisher ◽

Daniel J. Rubin

Keyword(s):

Logistic Regression ◽

Readmission Rate ◽

Medical Center ◽

Academic Medical Center ◽

Model Performance ◽

Readmission Rates ◽

Modeling Approaches ◽

Multiple Variables ◽

Highly Correlated ◽

Readmission Risk

Abstract Background There is little consensus on how to sample hospitalizations and analyze multiple variables to model readmission risk. The purpose of this study was to compare readmission rates and the accuracy of predictive models based on different sampling and multivariable modeling approaches. Methods We conducted a retrospective cohort study of 17,284 adult diabetes patients with 44,203 discharges from an urban academic medical center between 1/1/2004 and 12/31/2012. Models for all-cause 30-day readmission were developed by four strategies: logistic regression using the first discharge per patient (LR-first), logistic regression using all discharges (LR-all), generalized estimating equations (GEE) using all discharges, and cluster-weighted (CWGEE) using all discharges. Multiple sets of models were developed and internally validated across a range of sample sizes. Results The readmission rate was 10.2% among first discharges and 20.3% among all discharges, revealing that sampling only first discharges underestimates a population’s readmission rate. Number of discharges was highly correlated with number of readmissions (r = 0.87, P < 0.001). Accounting for clustering with GEE and CWGEE yielded more conservative estimates of model performance than LR-all. LR-first produced falsely optimistic Brier scores. Model performance was unstable below samples of 6000–8000 discharges and stable in larger samples. GEE and CWGEE performed better in larger samples than in smaller samples. Conclusions Hospital readmission risk models should be based on all discharges as opposed to just the first discharge per patient and utilize methods that account for clustered data.

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Impact of a pharmacy team–led intervention program on the readmission rate of elderly patients with heart failure

American Journal of Health-System Pharmacy ◽

10.2146/ajhp170256 ◽

2018 ◽

Vol 75 (4) ◽

pp. 183-190 ◽

Cited By ~ 9

Author(s):

Pamela M. Moye ◽

Pui Shan Chu ◽

Teresa Pounds ◽

Maria Miller Thurston

Keyword(s):

Heart Failure ◽

Older Patients ◽

Readmission Rate ◽

Academic Medical Center ◽

Hospital Readmissions ◽

Intervention Group ◽

Historical Control Group ◽

Historical Control ◽

Control Group ◽

Patients With Heart Failure

Purpose The results of a study to determine whether pharmacy team–led postdischarge intervention can reduce the rate of 30-day hospital readmissions in older patients with heart failure (HF) are reported. Methods A retrospective chart review was performed to identify patients 60 years of age or older who were admitted to an academic medical center with a primary diagnosis of HF during the period March 2013–June 2014 and received standard postdischarge follow-up care provided by physicians, nurses, and case managers. The rate of 30-day readmissions in that historical control group was compared with the readmission rate in a group of older patients with HF who were admitted to the hospital during a 15-month intervention period (July 2014–October 2015); in addition to usual postdischarge care, these patients received medication reconciliation and counseling from a team of pharmacists, pharmacy residents, and pharmacy students. Results Twelve of 97 patients in the intervention group (12%) and 20 of 80 patients in the control group (25%) were readmitted to the hospital within 30 days of discharge (p = 0.03); 11 patients in the control group (55%) and 7 patients in the intervention group (58%) had HF-related readmissions (p = 0.85). Conclusion In a population of older patients with HF, the rate of 30-day all-cause readmissions in a group of patients targeted for a pharmacy team–led postdischarge intervention was significantly lower than the all-cause readmission rate in a historical control group.

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Patient engagement with digital disease management and readmission rates: The case of congestive heart failure

Health Informatics Journal ◽

10.1177/14604582211030959 ◽

2021 ◽

Vol 27 (3) ◽

pp. 146045822110309

Author(s):

Rudin Gjeka ◽

Kirit Patel ◽

Chandra Reddy ◽

Nora Zetsche

Keyword(s):

Heart Failure ◽

Congestive Heart Failure ◽

Disease Management ◽

Readmission Rate ◽

Hospital Readmissions ◽

The Elderly ◽

Readmission Rates ◽

Management Platform ◽

High Readmission Rate ◽

Remote Patient

Congestive heart failure (CHF) is one of the most common diagnoses in the elderly United States Medicare (⩾ age 65) population. This patient population has a particularly high readmission rate, with one estimate of the 6-month readmission rate topping 40%. The rapid rise of mobile health (mHealth) presents a promising new pathway for reducing hospital readmissions of CHF, and, more generally, the management of chronic conditions. Using a randomized research design and a multivariate regression model, we evaluated the effectiveness of a hybrid mHealth model—the integration of remote patient monitoring with an applied health technology and digital disease management platform—on 45-day hospital readmissions for patients diagnosed with CHF. We find a 78% decrease in the likelihood of CHF hospital readmission for patients who were assigned to the digital disease management platform as compared to patients assigned to control.

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Abstract TP366: Significant Reduction in Readmission Using Transitions Nurse

Stroke ◽

10.1161/str.51.suppl_1.tp366 ◽

2020 ◽

Vol 51 (Suppl_1) ◽

Author(s):

Kiersten Espaillat ◽

Paula Buckner

Keyword(s):

Hospital Discharge ◽

Transitional Care ◽

Medical Center ◽

Hospital Readmissions ◽

Primary Diagnosis ◽

Adult Patients ◽

Care Coordinator ◽

Readmission Rates ◽

Before And After

In an effort to reduce early hospital readmissions, Vanderbilt University Medical Center (VUMC) implemented a transitional care coordinator (TCC) to provide careful coordinated follow up care for stroke patients after hospital discharge. The aim of this study is to compare all cause thirty- day readmission rates of adult patients with a primary diagnosis of stroke before and after the implementation of a stroke services TCC. All adult patients admitted to VUMC with a primary diagnosis of stroke; ischemic, hemorrhagic, and TIA; and readmitted within the first thirty days following hospital discharge between January-June of 2015, 2016, 2017, & 2018 were analyzed. Readmission data from 2015 & 2016, prior to the implementation of the TCC was compared to readmission data from 2017 & 2018, after the TCC was implemented. A total of 1911 charts were reviewed for the timeframe January-June of 2015-2018. In 2015 there were 369 stroke admissions and 120 (33%) were readmitted and in 2016 there were 474 stroke admissions and 112 (24%) readmissions, before the TCC role was implemented. In 2017 there were 540 stroke admissions and 62 (11%) were readmitted and in 2018 there were 528 stroke admissions and 74 (14%) readmissions, after the TCC role was implemented. Hospital readmissions were reduced significantly after implementing a TCC.

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Abstract WMP94: Readmission Within 30 days After Acute Ischemic Stroke, United States 2013

Stroke ◽

10.1161/str.48.suppl_1.wmp94 ◽

2017 ◽

Vol 48 (suppl_1) ◽

Author(s):

Xin Tong ◽

Mary G George ◽

Sallyann M Coleman King ◽

Cathleen Gillespie ◽

Robert Merritt

Keyword(s):

United States ◽

Ischemic Stroke ◽

Acute Ischemic Stroke ◽

Readmission Rate ◽

Hospital Readmissions ◽

The United States ◽

Artery Occlusion ◽

Principal Diagnosis ◽

Readmission Rates ◽

The Cost

Introduction: Hospital readmissions contribute significantly to the cost of medical care and reflect the burden of disease. Limited data have been reported on national hospital readmission after acute ischemic stroke. Methods: Among 2013 adult hospitalizations from the National Inpatient Sample of the Healthcare Cost and Utilization Project (HCUP) Nationwide Readmissions Database (NRD), we identified acute ischemic stroke (AIS) hospitalizations using principal diagnosis ICD-9-CM codes. We provided national estimates of AIS non-elective readmission rates within 30 days. Results: In 2013, there were a total of 489,813 adult index AIS admissions in the United States. The readmission rate within 30 days for a new AIS as the principal diagnosis was 2.1% of index AIS admissions, and was 10.2% of all readmissions. The readmission rate for all non-elective reasons increased with age, with the lowest readmission rate (8.9%) among ages 18-44, and the highest (11.7%).among ages 85+. The readmission rate was higher among patients with public insurance (11.1%) as compared to private (7.4%) or others (7.9%). Recurrent AIS (20.2%) was the most common reason for readmission, including unspecified cerebral artery occlusion with infarction (ICD9-CM=434.91, 13.0%) and cerebral embolism with infarction (ICD9-CM=434.11, 3.1%). In addition, infections were among the most common causes (Septicemia 5.7%, UTI 2.7%, and pneumonia 2.2%) and TIA (2.4%). Conclusions: The findings have important implications for identifying groups and conditions at high-risk for readmission. The large number of recurrent AIS within 30 days of index AIS highlights the need for improved patient follow-up and secondary prevention treatment.

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