Role of Post-Acute Care on Hospital Readmission After High-Risk Surgery.

BACKGROUND: Payment models, including the Hospital Readmissions Reduction Program and bundled payments, place pressures on hospitals to limit readmissions. Against this backdrop, we sought to investigate the association of post-acute care after major surgery and readmission rates. METHODS: We identified patients undergoing high-risk surgery (abdominal aortic aneurysm repair, coronary bypass grafting, aortic valve replacement, carotid endarterectomy, esophagectomy, pancreatectomy, lung resection, and cystectomy) from 2005 to 2010 using the Healthcare Cost and Utilization Project's State Inpatient Database. The primary outcome was readmission rates after major surgery. Secondary outcome was readmission length of stay. RESULTS: We identified 135,523 patients of whom 56,720 (42%) received post-acute care. Patients receiving post-acute care had higher readmission rates than those who were discharged home (16% versus 10%, respectively; P < 0.001). The risk-adjusted readmission length of stay was greatest for patients who received care from a skilled nursing facility, followed by those who received home care, and lowest for those who did not receive post-acute care (7.1 versus 5.4 versus 4.8 d, respectively; P < 0.001). CONCLUSIONS: The use of post-acute care was associated with higher readmission rates and higher readmission lengths of stay. Improving the support of patients in post-acute care settings may help reduce readmissions and readmission intensity.

Predictors and Cost of Readmission in Total Knee Arthroplasty.

BACKGROUND: The Comprehensive Care for Joint Replacement bundle was created to decrease total knee arthroplasty (TKA) cost. To help accomplish this, there is a focus on reducing TKA readmissions. However, there is a lack of national representative sample of all-payer hospital admissions to direct strategy, identify risk factors for readmission, and understand actual readmission cost. METHODS: We used the Nationwide Readmission Database to examine national readmission rates, predictors of readmission, and associated readmission costs for elective TKA procedures. We fit a multivariable logistic regression model to examine factors associated with readmission. Then, we determined mean readmission costs and calculated the readmission cost when distributed across the entire TKA population. RESULTS: We identified 224,465 patients having TKA across all states participating in the Nationwide Readmission Database. The mean unadjusted 30-day TKA readmission rate was 4%. The greatest predictors of readmission were congestive heart failure (odds ratio [OR] 2.51, 95% confidence interval [CI] 2.62-2.80), renal disease (OR 2.19, 95% CI 2.03-2.37), and length of stay greater than 4 days (OR 2.4, 95% CI 2.25-2.61). The overall median cost for each readmission was $6753 ± 175. Extrapolating the readmission cost for the entire TKA population resulted in the readmission cost being 2% of the overall 30-day procedure cost. CONCLUSIONS: A major focus of the Comprehensive Care for Joint Replacement bundle is improving cost and quality by limiting readmission rates. TKA readmissions are low and comprise a small percentage of total TKA cost, suggesting that they may not be the optimal measure of quality care or a significant driver of overall cost.

Variation in readmission expenditures after high-risk surgery.

BACKGROUND: The Hospital Readmissions Reduction Program reduces payments to hospitals with excess readmissions for three common medical conditions and recently extended its readmission program to surgical patients. We sought to investigate readmission intensity as measured by readmission cost for high-risk surgeries and examine predictors of higher readmission costs. MATERIALS AND METHODS: We used the Healthcare Cost and Utilization Project's State Inpatient Database to perform a retrospective cohort study of patients undergoing major chest (aortic valve replacement, coronary artery bypass grafting, lung resection) and major abdominal (abdominal aortic aneurysm repair [open approach], cystectomy, esophagectomy, pancreatectomy) surgery in 2009 and 2010. We fit a multivariable logistic regression model with generalized estimation equations to examine patient and index admission factors associated with readmission costs. RESULTS: The 30-d readmission rate was 16% for major chest and 22% for major abdominal surgery (P < 0.001). Discharge to a skilled nursing facility was associated with higher readmission costs for both chest (odds ratio [OR]: 1.99; 95% confidence interval [CI]: 1.60-2.48) and abdominal surgeries (OR: 1.86; 95% CI: 1.24-2.78). Comorbidities, length of stay, and receipt of blood or imaging was associated with higher readmission costs for chest surgery patients. Readmission >3 wk after discharge was associated with lower costs among abdominal surgery patients. CONCLUSIONS: Readmissions after high-risk surgery are common, affecting about one in six patients. Predictors of higher readmission costs differ among major chest and abdominal surgeries. Better identifying patients susceptible to higher readmission costs may inform future interventions to either reduce the intensity of these readmissions or eliminate them altogether.

A Model to Optimize Followup Care and Reduce Hospital Readmissions after Radical Cystectomy.

PURPOSE: Radical cystectomy has one of the highest readmission rates across all surgical procedures at approximately 25%. We developed a mathematical model to optimize outpatient followup regimens for radical cystectomy. MATERIALS AND METHODS: We used delay-time analysis, a systems engineering approach, to maximize the probability of detecting patients susceptible to readmission through office visits and telephone calls. Our data source includes patients readmitted after radical cystectomy from the Healthcare Cost and Utilization Project State Inpatient Databases in 2009 and 2010 as well as from our institutional bladder cancer database from 2007 to 2011. We measured the interval from hospital discharge to the point when a patient first exhibits concerning symptoms. Our primary end point is 30-day hospital readmission. Our model optimized the timing and sequence of followup care after radical cystectomy. RESULTS: The timing of office visits and telephone calls is more important in detecting a patient at risk for readmission than the sequence of these encounters. Patients are most likely to exhibit concerning symptoms between 4 and 5 days after discharge home. An optimally scheduled office visit can detect up to 16% of potential readmissions, which can be increased to 36% with 1 office visit followed by 4 telephone calls. CONCLUSIONS: Our model improves the detection of concerning symptoms after radical cystectomy by optimizing the timing and number of outpatient encounters. By understanding how to design better outpatient followup care for patients treated with radical cystectomy we can help reduce the readmission burden for this population.

Sharpening the focus on causes and timing of readmission after radical cystectomy for bladder cancer.

BACKGROUND: Readmissions after radical cystectomy are common, burdensome, and poorly understood. For these reasons, the authors conducted a population-based study that focused on the causes of and time to readmission after radical cystectomy. METHODS: Using Surveillance, Epidemiology, and End Results-Medicare data, at total of 1782 patients who underwent radical cystectomy from 2003 through 2009 were identified. A piecewise exponential model was used to examine reasons for readmission as well as patient and clinical factors associated with the timing of readmission. RESULTS: One in 4 patients (25.5%) were readmitted within 30 days of discharge after radical cystectomy. Compared with patients without readmission, those readmitted were similar with regard to age, sex, and race. Readmitted patients had more complications (33.8% vs 13.9%; P< .001) and were more likely to have been discharged to skilled nursing facilities from their index admission (P< .001). The average time to readmission and subsequent length of stay were 11.5 days and 6.7 days, respectively. The majority of readmissions (67.4%) occurred within 2 weeks of discharge, 66.8% had emergency department charges, and 25.9% involved intensive care unit use. Although the spectrum of reasons for readmission varied over the 4 weeks after discharge, the most common included infection (51.4%), failure to thrive (36.3%), and urinary (33.2%) and gastrointestinal (23.1%) etiologies; 95.8% of patients had ≥ 1 of these diagnosis groups present at the time of readmission. CONCLUSIONS: Readmissions after radical cystectomy are common and time-dependent. Interventions to prevent and reduce the readmission burden after cystectomy likely need to focus on the first 2 weeks after discharge, take into consideration the spectrum of reasons for readmission, and target high-risk individuals.

Using filtered forecasting techniques to determine personalized monitoring schedules for patients with open-angle glaucoma.

PURPOSE: To determine whether dynamic and personalized schedules of visual field (VF) testing and intraocular pressure (IOP) measurements result in an improvement in disease progression detection compared with fixed interval schedules for performing these tests when evaluating patients with open-angle glaucoma (OAG). DESIGN: Secondary analyses using longitudinal data from 2 randomized controlled trials. PARTICIPANTS: A total of 571 participants from the Advanced Glaucoma Intervention Study (AGIS) and the Collaborative Initial Glaucoma Treatment Study (CIGTS). METHODS: Perimetric and tonometric data were obtained for AGIS and CIGTS trial participants and used to parameterize and validate a Kalman filter model. The Kalman filter updates knowledge about each participant's disease dynamics as additional VF tests and IOP measurements are obtained. After incorporating the most recent VF and IOP measurements, the model forecasts each participant's disease dynamics into the future and characterizes the forecasting error. To determine personalized schedules for future VF tests and IOP measurements, we developed an algorithm by combining the Kalman filter for state estimation with the predictive power of logistic regression to identify OAG progression. The algorithm was compared with 1-, 1.5-, and 2-year fixed interval schedules of obtaining VF and IOP measurements. MAIN OUTCOME MEASURES: Length of diagnostic delay in detecting OAG progression, efficiency of detecting progression, and number of VF and IOP measurements needed to assess for progression. RESULTS: Participants were followed in the AGIS and CIGTS trials for a mean (standard deviation) of 6.5 (2.8) years. Our forecasting model achieved a 29% increased efficiency in identifying OAG progression (P<0.0001) and detected OAG progression 57% sooner (reduced diagnostic delay) (P = 0.02) than following a fixed yearly monitoring schedule, without increasing the number of VF tests and IOP measurements required. The model performed well for patients with mild and advanced disease. The model performed significantly more testing of patients who exhibited OAG progression than nonprogressing patients (1.3 vs. 1.0 tests per year; P<0.0001). CONCLUSIONS: Use of dynamic and personalized testing schedules can enhance the efficiency of OAG progression detection and reduce diagnostic delay compared with yearly fixed monitoring intervals. If further validation studies confirm these findings, such algorithms may be able to greatly enhance OAG management.

Comparison of readmission and early revision rates as a quality metric in total knee arthroplasty using the Nationwide Readmission Database.

BACKGROUND: After release of the Comprehensive Care for Joint Replacement bundle, there has been increased emphasis on reducing readmission rates for total knee arthroplasty (TKA). The potential for a separate, clinically-relevant metric, TKA revision rates within a year following surgery, has not been fully explored. Based on this, we compared rates and payments for TKA readmission and revision procedures as metrics for improving quality and cost. METHODS: We utilized the 2013 Nationwide Readmission Database (NRD) to examine national readmission and revision rates, the reasons for revision procedures, and associated costs for elective TKA procedures. As data are not linked across years, we examined revision rates for TKA completed in the month of January by capturing revision procedures in the subsequent following 11-month period to approximate a 1-year revision rate. Diagnosis and procedure codes for revision procedures were collected. Average readmission and revision procedure costs were then calculated, and the cost distributed across the entire TKA population. RESULTS: We identified 20,851 patients having TKA surgery. The mean unadjusted 30- and 90-day TKA readmission rates were 3.4% and 5.8%, respectively. In contrast, the mean unadjusted 3-month and approximate 1-year reoperation rates were 1.0% and 1.6%, respectively. The most common cause for revision was periprosthetic joint infection, which accounting for 62% of all reported revision procedures. The mean payment for 90-day readmission was roughly half ($10,589±$11,084) of the mean inpatient payment for single reoperation procedure at 90 days ($20,222±$17,799). Importantly, nearly half (46%) of all 90-day readmissions were associated with a reoperation event within the first year. CONCLUSIONS: Readmission following TKA is associated with a 1-year reoperation in approximately half of patients. These reoperations represent a significant patient burden and have a higher per episode cost. Early reoperation may represent a more clinically relevant target for quality improvement and cost containment.

Reframing Financial Incentives Around Reducing Readmission After Radical Cystectomy.

OBJECTIVE: To better understand the financial implications of readmission after radical cystectomy, an expensive surgery coupled with a high readmission rate. Currently, whether hospitals benefit financially from readmissions after radical cystectomy remains unclear, and potentially obscures incentives to invest in readmission reduction efforts. MATERIALS AND METHODS: Using a 20% sample of national Medicare beneficiaries, we identified 3544 patients undergoing radical cystectomy from January 2010 to November 2014. We compared price-standardized Medicare payments for index admissions and readmissions after surgery. We also examined the variable financial impact of length of stay and the proportion of Medicare payments coming from readmissions based on overall readmission rate. RESULTS: Medicare patients readmitted after cystectomy had higher index hospitalization payments ($19,164 readmitted vs $18,146 non-readmitted, P = .03) and an average readmission payment of $7356. Adjusted average Medicare readmission payments and length of stay varied significantly across hospitals, ranging from $2854 to $15,605, and 2.0 to 17.1 days, respectively (both P <.01), with longer length of stay associated with increased payments. After hospitals were divided into quartiles based on overall readmission rates, the percent of payments coming from readmissions ranged from 5% to 13%. CONCLUSION: Readmissions following radical cystectomy were associated with increased Medicare payments for the index hospitalization, and the readmission payment, potentially limiting incentives for readmission reduction programs. Our findings highlight opportunities to reframe efforts to support patients, caregivers, and providers through improving the discharge and readmission processes to create a patient-centered experience, rather than for fear of financial penalties.

An Integrated Framework for Reducing Hospital Readmissions using Risk Trajectories Characterization and Discharge Timing Optimization.

When patients leave the hospital for lower levels of care, they experience a risk of adverse events on a daily basis. The advent of value-based purchasing among other major initiatives has led to an increasing emphasis on reducing the occurrences of these post-discharge adverse events. This has spurred the development of new prediction technologies to identify which patients are at risk for an adverse event as well as actions to mitigate those risks. Those actions include pre-discharge and post-discharge interventions to reduce risk. However, traditional prediction models have been developed to support only post-discharge actions; predicting risk of adverse events at the time of discharge only. In this paper we develop an integrated framework of risk prediction and discharge optimization that supports both types of interventions: discharge timing and post-discharge monitoring. Our method combines a kernel approach for capturing the non-linear relationship between length of stay and risk of an adverse event, with a Principle Component Analysis method that makes the resulting estimation tractable. We then demonstrate how this prediction model could be used to support both types of interventions by developing a simple and easily implementable discharge timing optimization.

Dynamic Monitoring and Control of Irreversible Chronic Diseases with Application to Glaucoma.

To manage chronic disease patients effectively, clinicians must know (1) how to monitor each patient (i.e., when to schedule the next visit and which tests to take), and (2) how to control the disease (i.e., what levels of controllable risk factors will sufficiently slow progression). Our research addresses these questions simultaneously and provides the optimal solution to a novel linear quadratic Gaussian state space model. For the objective of minimizing the relative change in state over time (i.e., disease progression), which is necessary for managing irreversible chronic diseases while also considering the cost of tests and treatment, we show that the classical two-way separation of estimation and control holds. This makes a previously intractable problem solvable by decomposition into two separate, tractable problems while maintaining optimality. The resulting optimization is applied to the management of glaucoma. Based on data from two large randomized clinical trials, we validate our model and demonstrate how our decision support tool can provide actionable insights to the clinician caring for a patient with glaucoma. This methodology can be applied to a broad range of irreversible chronic diseases to devise patient-specific monitoring and treatment plans optimally.

Inaugural Readmission Penalties for Total Hip and Total Knee Arthroplasty Procedures Under the Hospital Readmissions Reduction Program.

Importance: The Hospital Readmissions Reduction Program (HRRP) is a Centers for Medicare and Medicaid Services policy that levies hospital reimbursement penalties based on excess readmissions of patients with 4 medical conditions and 3 surgical procedures. A greater understanding of factors associated with the 3 surgical reimbursement penalties is needed for clinicians in surgical practice. Objective: To investigate the first year of HRRP readmission penalties applied to 2 surgical procedures-elective total hip arthroplasty (THA) and total knee arthroplasty (TKA)-in the context of hospital and patient characteristics. Design, Setting, and Participants: Fiscal year 2015 HRRP penalization data from Hospital Compare were linked with the American Hospital Association Annual Survey and with the Healthcare Cost and Utilization Project State Inpatient Database for hospitals in the state of Florida. By using a case-control framework, those hospitals were separated based on HRRP penalty severity, as measured with the HRRP THA and TKA excess readmission ratio, and compared according to orthopedic volume as well as hospital-level and patient-level characteristics. The first year of HRRP readmission penalties applied to surgery in Florida Medicare subsection (d) hospitals was examined, identifying 60 663 Medicare patients who underwent elective THA or TKA in 143 Florida hospitals. The data analysis was conducted from February 2016 to January 2017. Exposures: Annual hospital THA and TKA volume, other hospital-level characteristics, and patient factors used in HRRP risk adjustment. Main Outcomes and Measures: The HRRP penalties with HRRP excess readmission ratios were measured, and their association with annual THA and TKA volume, a common measure of surgical quality, was evaluated. The HRRP penalties for surgical care according to hospital and readmitted patient characteristics were then examined. Results: Among 143 Florida hospitals, 2991 of 60 663 Medicare patients (4.9%) who underwent THA or TKA were readmitted within 30 days. Annual hospital arthroplasty volume seemed to follow an inverse association with both unadjusted readmission rates (r = -0.16, P = .06) and HRRP risk-adjusted readmission penalties (r = -0.12, P = .14), but these associations were not statistically significant. Other hospital characteristics and readmitted patient characteristics were similar across HRRP orthopedic penalty severity. Conclusions and Relevance: This study's findings suggest that higher-volume hospitals had less severe, but not significantly different, rates of readmission and HRRP penalties, without systematic differences across readmitted patients.

A Capacity Allocation Planning Model for Integrated Care and Access Management.

The prevailing first-come-first-served approach to outpatient appointment scheduling ignores differing urgency levels, leading to unnecessarily long waits for urgent patients. In data from a partner healthcare organization, we found in some departments that urgent patients were inadvertently waiting longer for an appointment than non-urgent patients. This paper develops a capacity allocation optimization methodology that reserves appointment slots based on urgency in a complicated, integrated care environment where multiple specialties serve multiple types of patients. This optimization reallocates network capacity to limit access delays (indirect waiting times) for initial and downstream appointments differentiated by urgency. We formulate this problem as a queueing network optimization and approximate it via deterministic linear optimization to simultaneously smooth workloads and guarantee access delay targets. In a case study of our industry partner we demonstrate the ability to (1) reduce urgent patient mean access delay by 27% with only a 7% increase in mean access delay for non-urgent patients, and (2) increase throughput by 31% with the same service levels and overtime.

The Fate of Radical Cystectomy Patients after Hospital Discharge: Understanding the Black Box of the Pre-readmission Interval.

BACKGROUND: Radical cystectomy has one of the highest 30-d hospital readmission rates but circumstances leading to readmission remain poorly understood. OBJECTIVE: To examine the postdischarge period and better understand hospital readmission after radical cystectomy. DESIGN, SETTING, AND PARTICIPANTS: We conducted a retrospective cohort study of patients treated with radical cystectomy for bladder cancer from 2005 to 2012 using our institutional database. OUTCOME MEASUREMENTS AND STATISTICAL ANALYSIS: We assessed patient communication with any healthcare system after hospital discharge based on timing, methods, and concern types. Logistic regression and Cox proportional-hazards analyses were used to compare postdischarge concerns among readmitted and nonreadmitted patients. We internally validated the logistic model using a bootstrap resampling technique. RESULTS AND LIMITATIONS: One-hundred patients (23%) were readmitted within 30 d of index discharge. Readmitted patients were more likely to use the emergency department with initial concerns compared with nonreadmitted patients (27% vs 1.0%, p<0.001). Patients who took longer to first communicate their concerns and who were able to tolerate their symptoms longer had lower odds of readmission. Patients who reported infection (adjusted hazard ratio: 2.8, 95% confidence interval: 1.4-5.8) and failure to thrive concerns (adjusted hazard ratio: 4.4, 95% confidence interval: 2.0-9.3) were more likely to be readmitted compared with those who communicated noninfectious wounds and/or urinary concerns. CONCLUSIONS: Radical cystectomy patients who contact the health system soon after discharge or communicated infectious or failure to thrive symptoms (fever, poor oral intake, or vomiting) are more likely to experience readmission as opposed to those that endorse pain, constipation, or ostomy issues. Better understanding of this pre-readmission interval can optimize postdischarge practices. PATIENT SUMMARY: We looked at bladder cancer patients who had surgery and the reasons why they were readmitted to hospital. We found patients who had a fever or difficulty with eating and maintaining their weight had the highest chance of being readmitted.

No Differences in Population-based Readmissions After Open and Robotic-assisted Radical Cystectomy: Implications for Post-discharge Care.

OBJECTIVE: To inform whether readmission reduction strategies should consider surgical approach, we examined readmission differences between open and robotic-assisted radical cystectomy (RARC) using population-based data. METHODS: We identified patients who underwent cystectomy between January 2010 and September 2013 based on International Classification of Diseases-9th edition codes and administrative claims from a large, national US health insurer (Clinformatics Data Mart Database, OptumInsight, Eden Prairie, MN). We assessed post-discharge health system utilization and tested for differences in readmissions after the 2 surgical approaches. RESULTS: We identified 935 patients treated with cystectomy: open = 785 (84%) and RARC = 150 (16%). Patients undergoing RARC were slightly older, male, had more ileal conduit urinary reconstruction, and less need for intensive care. Index length of stay was shorter for RARC than for open surgery (7 days vs 8 days, P < .001). However, we found no differences in 30-day readmission rates (24% open vs 29% RARC, P = .26) or other readmission parameters, including readmission length of stay (5 days open vs 4 days RARC, P = .32), emergency department use (22% open vs 24% RARC, P = .86), reasons for readmission, or timing of first outpatient visits (11.5 days open vs 9 days RARC, P = .41). For both approaches, the majority of patients were readmitted within 2 weeks. CONCLUSION: The surgical approach to cystectomy does not appear to impact readmissions. Strategies to reduce the readmission burden after cystectomy do not need to consider surgical approach but should focus on timing of medical contacts.

An IP-based healthcare provider shift design approach to minimize patient handoffs.

The new Accreditation Council for Graduate Medical Education (ACGME) duty-hour standards for residents and fellows went into effect in 2011. These regulations were designed to reduce fatigue-related medical errors and improve patient safety. The new shift restrictions, however, have led to more frequent transitions in patient care (handoffs), resulting in greater opportunity for communication breakdowns between caregivers, which correlate with medical errors and adverse events. Recent research has focused on improving the quality of these transitions through standardization of the handoff protocols; however, no attention has been given to reducing the number of transitions in patient care. This research leverages integer programming methods to design a work shift schedule for trainees that minimizes patient handoffs while complying with all ACGME duty-hour standards, providing required coverage, and maintaining physician quality of life. In a case study of redesigning the trainees' schedule for a Mayo Clinic Medical Intensive Care Unit (MICU), we show that the number of patient handoffs can be reduced by 23 % and still meet all required and most desired scheduling constraints. Furthermore, a 48 % reduction in handoffs could be achieved if only the minimum required rules are satisfied.