Do pneumonia readmissions flagged as potentially preventable by the 3M PPR software have more process of care problems? A cross-sectional observational study

A Case Manager-Led Pneumonia Care Bundle in a Subacute Rehabilitation Facility

PURPOSE OF THE STUDY

To evaluate the relationship between a case manager-led pneumonia care bundle at skilled nursing facilities (SNFs) and 30-day hospital readmissions for pneumonia.

PRIMARY PRACTICE SETTINGS

The primary practice settings included patients hospitalized with pneumonia at 2 community hospitals between October 2018 and June 2019 and who were subsequently transferred to an SNF.

METHODOLOGY AND SAMPLE

A retrospective cohort study was completed comparing patients in the preintervention cohort who received pneumonia standard of care versus patients in the postintervention cohort who received a case manager-led evidence-based pneumonia care bundle at an SNF. From October 2018 to June 2019, patients admitted with pneumonia to 2 community hospitals in Northwest New Jersey were enrolled in the preintervention cohort. Patients admitted with pneumonia from January 2020 to June 2021 were enrolled in the postintervention group. The primary outcome was to reduce 30-day readmission rates for all patients discharged from the hospital to an SNF with pneumonia.

RESULTS

Ninety-nine patients were enrolled in the preintervention cohort and 34 patients were enrolled in the postinterventions cohort. Thirty-day readmission rates were lower in the postintervention cohort (24.2% vs. 17.7%). This reduction in readmission rates was clinically significant, demonstrating a 27% reduction for all patients discharged from the hospital to an SNF with pneumonia.

IMPLICATIONS FOR CASE MANAGEMENT PRACTICE

Individualized pneumonia self-management education can be easily implemented in SNFs to improve quality-of-care outcomes for patients. Our health care system collaborates with several SNFs to decrease 30-day hospital readmission. The pneumonia care bundle includes specific measures to improve the transition of care for patients with pneumonia by decreasing the variability of patient care after discharge from the hospital to an SNF. It was hypothesized that to decrease readmissions from the SNFs, we needed to address the quality of care provided by the SNFs by using a 2-prong approach; education of SNF staff on the pneumonia care bundle, and in-person weekly follow-up visits in the SNF until discharge from the SNF to the patient's home.

Comparing Software Determination of Readmission Preventability With Chart Review, Provider, and Family Assessments

OBJECTIVES

To explore the concordance between software, chart reviewer, provider, and parent perspectives when assessing whether readmissions are preventable or clinically related to the initial admission.

METHODS

Providers and parents of patients readmitted within 3 days to a tertiary children's hospital were enrolled in this single-site observational study. 3M Potentially Preventable Readmissions Grouping Software, chart reviewers, discharge and readmission providers, and parents assessed if readmissions were clinically related to the index admission or potentially preventable. Agreement between perspectives was measured by using Cohen's κ values.

RESULTS

The software found 67 of 118 (57%) clinically related readmissions; the identical 67 of 118 cases (57%) were found to be potentially preventable. Chart reviewers found 107 of 125 (86%) clinically related and 60 of 125 (47%) preventable readmissions compared to 68 of 92 (74%) and 27 of 92 (28%) for discharge physicians and 69 of 93 (74%) and 33 of 93 (34%) for readmitting physicians. Parents reported 9 of 36 (25%) preventable readmissions. Cohen κ values revealed no to minimal agreement on clinical relatedness of readmissions between software and chart reviewer, discharge provider, and readmission provider (0.12-0.20), whereas chart reviewers and providers had weak to moderate agreement with each other (0.43-0.75). There was no to minimal agreement on preventability between software and the other perspectives (-0.04 to 0.21), whereas chart reviewers and providers had minimal to weak agreement (0.27-0.56).

CONCLUSIONS

Measurement of preventable readmissions remains problematic, and using financial penalties for readmissions on the basis of software determinations may be unwise given low levels of agreement. Chart review supplemented by information from providers and families offers a more inclusive way to identify potentially preventable readmissions.

Towards a patient journey perspective on causes of unplanned readmissions using a classification framework: results of a systematic review with narrative synthesis

BACKGROUND

Several literature reviews have been published focusing on the prevalence and/or preventability of hospital readmissions. To our knowledge, none focused on the different causes which have been used to evaluate the preventability of readmissions. Insight into the range of causes is crucial to understand the complex nature of readmissions. We conducted a systematic review to: (1) evaluate the range of causes of unplanned readmissions in a patient journey, and (2) present a cause classification framework that can support future readmission studies.

METHODS

A literature search was conducted in PUBMED and EMBASE using "readmission" and "avoidability" or "preventability" as key terms. Studies that specified causes of unplanned readmissions were included. The causes were classified into eight preliminary root causes: Technical, Organization (integrated care), Organization (hospital department level), Human (care provider), Human (informal caregiver), Patient (self-management), Patient (disease), and Other. The root causes were based on expert opinions and the root cause analysis tool of PRISMA (Prevention and Recovery Information System for Monitoring and Analysis). The range of different causes were analyzed using Microsoft Excel.

RESULTS

Forty-five studies that reported 381 causes of readmissions were included. All studies reported causes related to organization of care at the hospital department level. These causes were often reported as preventable. Twenty-two studies included causes related to patient's self-management and 19 studies reported causes related to patient's disease. Studies differed in which causes were seen as preventable or unpreventable. None reported causes related to technical failures and causes due to integrated care issues were reported in 18 studies.

CONCLUSIONS

This review showed that causes for readmissions were mainly evaluated from a hospital perspective. However, causes beyond the scope of the hospital can also play a major role in unplanned readmissions. Opinions regarding preventability seem to depend on contextual factors of the readmission. This study presents a cause classification framework that could help future readmission studies to gain insight into a broad range of causes for readmissions in a patient journey.

Association of Postoperative Readmissions With Surgical Quality Using a Delphi Consensus Process to Identify Relevant Diagnosis Codes

Importance

Postoperative readmission data are used to measure hospital performance, yet the extent to which these readmissions reflect surgical quality is unknown.

Objective

To establish expert consensus on whether reasons for postoperative readmission are associated with the quality of surgery in the index admission.

Design, Setting, and Participants

In a modified Delphi process, a panel of 14 experts in medical and surgical readmissions comprising physicians and nonphysicians from Veterans Affairs (VA) and private-sector institutions reviewed 30-day postoperative readmissions from fiscal years 2008 through 2014 associated with inpatient surgical procedures performed at a VA medical center between October 1, 2007, and September 30, 2014. The consensus process was conducted from January through May 2017. Reasons for readmission were grouped into categories based on International Classification of Diseases, Ninth Revision (ICD-9) diagnosis codes. Panelists were given the proportion of readmissions coded by each reason and median (interquartile range) days to readmission. They answered the question, "Does the readmission reason reflect possible surgical quality of care problems in the index admission?" on a scale of 1 (never related) to 5 (directly related) in 3 rounds of consensus building. The consensus process was completed in May 2017 and data were analyzed in June 2017.

Main Outcomes and Measures

Consensus on proportion of ICD-9-coded readmission reasons that reflected quality of surgical procedure.

Results

In 3 Delphi rounds, the 14 panelists achieved consensus on 50 reasons for readmission; 12 panelists also completed group telephone calls between rounds 1 and 2. Readmissions with diagnoses of infection, sepsis, pneumonia, hemorrhage/hematoma, anemia, ostomy complications, acute renal failure, fluid/electrolyte disorders, or venous thromboembolism were considered associated with surgical quality and accounted for 25 521 of 39 664 readmissions (64% of readmissions; 7.5% of 340 858 index surgical procedures). The proportion of readmissions considered to be not associated with surgical quality varied by procedure, ranging from to 21% (613 of 2331) of readmissions after lower-extremity amputations to 47% (745 of 1598) of readmissions after cholecystectomy.

Conclusions and Relevance

One-third of postoperative readmissions are unlikely to reflect problems with surgical quality. Future studies should test whether restricting readmissions to those with specific ICD-9 codes might yield a more useful quality measure.

Which Readmissions May Be Preventable? Lessons Learned From a Posthospitalization Care Transitions Program for High-risk Elders

BACKGROUND

Care transitions programs have been shown to reduce hospital readmissions.

OBJECTIVES

The main objective of this study was to evaluate effects of the Mayo Clinic Care Transitions (MCCTs) Program on potentially preventable and nonpreventable 30-day unplanned readmissions among high-risk elders.

RESEARCH DESIGN

This was a retrospective cohort study of patients enrolled in MCCT following hospitalization and propensity score-matched controls receiving usual primary care.

SUBJECTS

The subjects were primary care patients, who were 60 years or older, at high-risk for readmission, and hospitalized for any cause between January 1, 2011 and June 30, 2013.

MEASURES

Hospital readmission within 30 days. The 3M algorithm was used to identify potentially preventable readmissions. Readmissions for ambulatory care sensitive conditions, a subset of preventable readmissions identified by the 3M algorithm, were also assessed.

RESULTS

The study cohort included 365 pairs of MCCT enrollees and propensity score-matched controls. Patients were similar in age (mean 83 y) and other baseline demographic and clinical characteristics, including reason for index hospitalization. MCCT enrollees had a significantly lower all-cause readmission rate [12.4% (95% confidence interval: CI, 8.9-15.7) vs. 20.1% (15.8-24.1); P=0.004] resulting from a decrease in potentially preventable readmissions [8.4% (95% CI, 5.5-11.3) vs. 14.3% (95% CI, 10.5-17.9); P=0.01]. Few potentially preventable readmissions were for ambulatory care sensitive conditions (6.7% vs. 12.0%). The rates of nonpotentially preventable readmissions were similar [4.3% (95% CI, 2.2-6.5) vs. 6.7% (95% CI, 4.0-9.4); P=0.16]. Potentially preventable readmissions were reduced by 44% (hazard ratio, 0.56; 95% CI, 0.36-0.88; P=0.01) with no change in other readmissions.

CONCLUSIONS

The MCCT significantly reduces preventable readmissions, suggesting that access to multidisciplinary care can reduce readmissions and improve outcomes for high-risk elders.

Assessment of Avoidable Readmissions in a Visceral Surgery Department with an Algorithm: Methodology, Analysis and Measures for Improvement

BACKGROUND

Standardized quality indicators assessing avoidable readmission become increasingly important in health care. They can identify improvements area and contribute to enhance the care delivered. However, the way of using them in practice was rarely described.

METHODS

Retrospective study uses prospective inpatients' information. Thirty-day readmissions were deemed potentially avoidable or non-avoidable by a computerized algorithm, and annual rate was reported between 2010 and 2014. Observed rate was compared to expected rate, and medical record review of potentially avoidable readmissions was conducted on data between January and June 2014.

RESULTS

During a period of ten semesters, 11,011 stays were screened by the algorithm and a potentially avoidable readmission rate (PAR) of 7% was measured. Despite stable expected rate of 5 ± 0.5%, an increase was noted concerning the observed rate since 2012, with a highest value of 9.4% during the first semester 2014. Medical chart review assessed the 109 patients screened positive for PAR during this period and measured a real rate of 7.8%. The delta was in part due to an underestimated case mix owing to sub-coded comorbidities and not to health care issue.

CONCLUSIONS

The present study suggests a methodology for practical use of data, allowing a validated quality of care indicator. The trend of the observed PAR rate showed a clear increase, while the expected PAR rate was stable. The analysis emphasized the importance of adequate "coding chain" when such an algorithm is applied. Moreover, additional medical chart review is needed when results deviate from the norm.

To what degree can variations in readmission rates be explained on the level of the hospital? a multilevel study using a large Dutch database

BACKGROUND

It is not clear which part of the variation in hospital readmissions can be attributed to the standard of care hospitals provide. This is in spite of their widespread use as an indicator of a lower quality of care. The aim of this study is to assess the variation in readmissions on the hospital level after adjusting for case-mix factors.

METHODS

We performed multilevel logistic regression analyses with a random intercept for the factor 'hospital' to estimate the variance on the hospital level after adjustment for case-mix variables. We used administrative data from 53 Dutch hospitals from 2010 to 2012 (58% of all Dutch hospitals; 2,577,053 admissions). We calculated models for the top ten diagnosis groups with the highest number of readmissions after an index admission for a surgical procedure. We calculated intraclass correlation coefficients (ICC) per diagnosis group in order to explore the variation in readmissions between hospitals. Furthermore, we determined C-statistics for the models with and without a random effect on the hospital level to determine the discriminative ability.

RESULTS

The ICCs on the hospital level ranged from 0.48 to 2.70% per diagnosis group. The C-statistics of the models with a random effect on the hospital level ranged from 0.58 to 0.65 for the different diagnosis groups. The C-statistics of the models that included the hospital level were higher compared to the models without this level.

CONCLUSIONS

For some diagnosis groups, a small part of the explained variation in readmissions was found on the hospital level, after adjusting for case-mix variables. However, the C-statistics of the prediction models are moderate, so the discriminative ability is limited. Readmission indicators might be useful for identifying areas for improving quality within hospitals on the level of diagnosis or specialty.

Determining best outcomes from community-acquired pneumonia and how to achieve them

Community-acquired pneumonia (CAP) is a common acute medical illness with a standard, effective treatment that was introduced before the evidenced-based medicine era. Mortality rates have improved in recent decades but improvements have been minimal when compared to other conditions such as acute coronary syndromes. The standardized approach to treatment makes CAP a target for comparative performance and outcome measures. While easy to collect, simplistic outcomes such as mortality, readmission and length of stay are difficult to interpret as they can be affected by subjective choices and health care resources. Proposed clinical- and patient-reported outcomes are discussed below and include measures such as the time to clinical stability (TTCS) and patient satisfaction, which can be compared between health institutions. Strategies to improve these outcomes include use of a risk stratification tool, local antimicrobial guidelines with antibiotic stewardship and care bundles to include early administration of antibiotics and early mobilization.

Understanding Long-Term Outcomes Following Sepsis: Implications and Challenges

Sepsis is life-threating organ dysfunction due to infection. Incidence of sepsis is increasing and the short-term mortality is improving, generating more sepsis survivors. These sepsis survivors suffer from additional morbidities such as higher risk of readmissions, cardiovascular disease, cognitive impairment and of death, for years following index sepsis episode. In the first year following index sepsis episode, approximately 60 % of sepsis survivors have at least one rehospitalisation episode, which is most often due to infection and one in six sepsis survivors die. Sepsis survivors also have a higher risk of cognitive impairment and cardiovascular disease contributing to the reduced life expectancy seen in this population, when assessed with life table comparisons. For optimal design of interventional trials to reduce these bad outcomes in sepsis survivors, in-depth understanding of major risk factors for these morbid events, their modifiability and a causal relationship to the pathobiology of sepsis is essential. This review highlights the recent advances, clinical and methodological challenges in our understanding of these morbid events in sepsis survivors.

Do Acute Myocardial Infarction and Heart Failure Readmissions Flagged as Potentially Preventable by the 3M Potentially Preventable Readmissions Software Have More Process-of-Care Problems?

BACKGROUND

The 3M Potentially Preventable Readmissions (3M-PPR) software matches clinically related index admission and readmission diagnoses that may signify in-hospital or postdischarge quality problems. To assess whether the PPR algorithm identifies preventable readmissions, we compared processes of care between PPR software-flagged and nonflagged cases.

METHODS AND RESULTS

Using 2006 to 2010 national VA administrative data, we identified acute myocardial infarction and heart failure discharges associated with 30-day all-cause readmissions, then flagged cases (PPR-Yes/PPR-No) using the 3M-PPR software. To assess care quality, we abstracted medical records of 100 readmissions per condition using tools containing explicit processes organized into admission work-up, in-hospital evaluation/treatment, discharge readiness, postdischarge period. We derived quality scores, scaled to a maximum of 25 per section (maximum total score=100) and compared cases on total and section-specific mean scores. For acute myocardial infarction, 77 of 100 cases were flagged as PPR-Yes. Section quality scores were highest for in-hospital evaluation/treatment (20.5±2.8) and lowest for postdischarge care (6.8±9.1). Total and section-related mean scores did not differ by PPR status; respective PPR-Yes versus PPR-No total scores were 61.6±11.1 and 60.4±9.4; P=0.98. For heart failure, 86 of 100 cases were flagged as PPR-Yes. Section scores were highest for discharge readiness (18.8±2.4) and lowest for postdischarge care (7.3±8.1). Like acute myocardial infarction, total and section-related mean scores did not differ by PPR status; PPR-Yes versus PPR-No total scores were 61.2±10.8 and 63.4±7.0, respectively; P=0.47.

CONCLUSIONS

Among VA acute myocardial infarction and heart failure readmissions, the 3M-PPR software does not distinguish differences in case-level quality of care. Whether 3M-PPR software better identifies preventable readmissions by using other methods to capture poorly documented processes or performing different comparisons requires further study.