Quality improvement lessons learned from National Implementation of the "Patient Safety Events in Community Care: Reporting, Investigation, and Improvement Guidebook".

OBJECTIVE: To evaluate nationwide implementation of a Guidebook designed to standardize safety practices across VA-delivered and VA-purchased care (i.e., Community Care) and identify lessons learned and strategies to improve them. DATA SOURCES AND STUDY SETTING: Qualitative data collected from key informants at 18 geographically diverse VA facilities across 17 Veterans Integrated Services Networks (VISNs). STUDY DESIGN: We conducted semi-structured interviews from 2019 to 2022 with VISN Patient Safety Officers (PSOs) and VA facility patient safety and quality managers (PSMs and QMs) and VA Facility Community Care (CC) staff to assess lessons learned by examining organizational contextual factors affecting Guidebook implementation based on the Consolidated Framework for Implementation Research (CFIR). DATA COLLECTION/EXTRACTION METHODS: Interviews were conducted virtually with 45 facility staff and 10 VISN PSOs. Using directed content analysis, we identified CFIR factors affecting implementation. These factors were mapped to the Expert Recommendations for Implementing Change (ERIC) strategy compilation to identify lessons learned that could be useful to our operational partners in improving implementation processes. We met frequently with our partners to discuss findings and plan next steps. PRINCIPAL FINDINGS: Six CFIR constructs were identified as both facilitators and barriers to Guidebook implementation: (1) planning for implementation; (2) engaging key knowledge holders; (3) available resources; (4) networks and communications; (5) culture; and (6) external policies. The two CFIR constructs that were only barriers included: (1) cosmopolitanism and (2) executing implementation. CONCLUSIONS: Our findings suggest several important lessons: (1) engage all collaborators involved in implementation; (2) ensure end-users have opportunities to provide feedback; (3) describe collaborators' purpose and roles/responsibilities clearly at the start; (4) communicate information widely and repeatedly; and (5) identify how multiple high priorities can be synergistic. This evaluation will help our partners and key VA leadership to determine next steps and future strategies for improving Guidebook implementation through collaboration with VA staff.

Facilitators and Barriers to Verifying Penicillin Allergies in a Veteran Nursing Home Population.

BACKGROUND: Unconfirmed penicillin allergies are common and may contribute to adverse outcomes, especially in frail older patients. Evidence-based clinical pathways for evaluating penicillin allergies have been effectively and safely applied in selected settings, but not in nursing home populations. OBJECTIVE: To identify potential facilitators and barriers to implementing a strategy to verify penicillin allergies in Veterans Health Administration nursing homes, known as Community Living Centers (CLCs). METHODS: We conducted semistructured interviews with staff, patients, and family members at 1 CLC to assess their understanding of penicillin allergies and receptiveness to verifying the allergy. We also asked staff about the proposed allergy assessment strategy, including willingness to delabel by history and feasibility of performing oral challenges or skin testing on their unit. RESULTS: From 24 interviews (11 front-line staff, 4 leadership, 3 patients, 6 family members), we identified several facilitators or barriers. Staff recognized the importance of allergy verification and were willing to support and assist in implementing verification strategies. The CLC residents were willing to have their allergy status verified. However, some family members expressed reluctance to verifying their relative's allergy status owing to safety concerns. Front-line staff also expressed concern over having the necessary resources, including time and expertise, to implement the strategy. Staff suggested involving clinical pharmacists and educating staff, patients, and family members as ways to overcome these barriers. CONCLUSIONS: Concerns about safety and staff resources are important potential barriers to implementing verification strategies. Involvement of pharmacists and education of both staff and patients and family members will be important components of any successful intervention.

Development and Validation of Quality Measures for Testosterone Prescribing.

Context: Accurate measures to assess appropriateness of testosterone prescribing are needed to improve prescribing practices. Objective: This work aimed to develop and validate quality measures around the initiation and monitoring of testosterone prescribing. Methods: This retrospective cohort study comprised a national cohort of male patients receiving care in the Veterans Health Administration who initiated testosterone during January or February 2020. Using laboratory data and diagnostic codes, we developed 9 initiation and 7 monitoring measures. These were based on the current Endocrine Society guidelines supplemented by expert opinion and prior work. We chose measures that could be operationalized using national VA electronic health record (EHR) data. We assessed criterion validity for these 16 measures by manual review of 142 charts. Main outcome measures included positive and negative predictive values (PPVs, NPVs), overall accuracy (OA), and Matthews Correlation Coefficients (MCCs). Results: We found high PPVs (>78%), NPVs (>98%), OA (≥94%), and MCCs (>0.85) for the 10 measures based on laboratory data (5 initiation and 5 monitoring). For the 6 measures relying on diagnostic codes, we similarly found high NPVs (100%) and OAs (≥98%). However, PPVs for measures of acute conditions occurring before testosterone initiation (ie, acute myocardial infarction or stroke) or new conditions occurring after initiation (ie, prostate or breast cancer) PPVs were much lower (0% to 50%) due to few or no cases. Conclusion: We developed several valid EHR-based quality measures for assessing testosterone-prescribing practices. Deployment of these measures in health care systems can facilitate identification of quality gaps in testosterone-prescribing and improve care of men with hypogonadism.

Beyond Medication Adherence: The Role of Patients' Beliefs and Life Context in Blood Pressure Control.

Objective: Despite numerous interventions to address adherence to antihypertensive medications, continued high rates of uncontrolled blood pressure (BP) suggest a need to better understand patient factors beyond adherence associated with BP control. We examined how patients' BP-related beliefs, and aspects of life context affect BP control, beyond medication adherence. Methods: We conducted a cross-sectional telephone survey of primary care patients with hypertension between 2010 and 2011 (N=103; 93 had complete data on all variables and were included in the regression analyses). We assessed patient sociodemographics (including race/ethnicity), medication adherence, BP-related beliefs, aspects of life context, and used clinical BP assessments. Results: Regression models including sociodemographics, medication adherence, and either beliefs or context consistently predicted BP control. Adding context after beliefs added no predictive value while adding beliefs after context significantly predicted BP control. Practical Implications: Results suggest that when clinicians must choose a dimension on which to intervene, focusing on beliefs would be the most fruitful approach to effecting change in BP control.

The Patient Safety Indicator Perioperative Pulmonary Embolism or Deep Vein Thrombosis: Is there associated surveillance bias in the Veterans Health Administration?

BACKGROUND: Studies disagree whether surveillance bias is associated with perioperative venous thromboembolism (VTE) performance measures. A prior VA study used a chart-based outcome; no studies have used the fully specified administrative data-based AHRQ Patient Safety Indicator, PSI-12, as their primary outcome. If surveillance bias were present, we hypothesized that inpatient surveillance rates would be associated with higher PSI-12 rates, but with lower post-discharge VTE rates. METHODS: Using VA data, we examined Pearson correlations between hospital-level VTE imaging rates and risk-adjusted PSI-12 rates and post-discharge VTE rates. To determine the robustness of findings, we conducted several sensitivity analyses. RESULTS: Hospital imaging rates were positively correlated with both PSI-12 (r = 0.24, p = 0.01) and post-discharge VTE rates (r = 0.16, p = 0.09). Sensitivity analyses yielded similar findings. CONCLUSIONS: Like the prior VA study, we found no evidence of PSI-12-related surveillance bias. Given the use of PSI-12 in nationwide measurement, these findings warrant replication using similar methods in the non-VA setting.

Does Surveillance Bias Influence the Validity of Measures of Inpatient Complications? A Systematic Review.

Surveillance bias may threaten the accuracy of inpatient complication measures. A systematic literature review was conducted to examine whether surveillance bias influences the validity of selected Patient Safety Indicator- and health care associated infection-related measures. Ten venous thromboembolism (VTE) articles were identified: 7 trauma related, 3 postoperative, and 1 central line-associated bloodstream infection (CLABSI) article. Nine VTE articles found positive associations between deep vein thrombosis imaging and VTE diagnoses. Because imaging also may be symptom driven, most studies performed additional analyses to corroborate findings. Six trauma-related and 2 postoperative VTE studies concluded that surveillance bias was present, the latter based on circumstantial evidence. The non-VTE study found a significant positive correlation between surveillance aggressiveness and intensive care unit CLABSI rates. Even considering VTE, relatively little is known about the impact of surveillance bias on inpatient complication measures. Given the implications of misclassifying hospitals on quality, this issue requires further investigation using more direct measurement methods.

Results of a Regional Effort to Improve Warfarin Management.

BACKGROUND: Improved anticoagulation control with warfarin reduces adverse events and represents a target for quality improvement. No previous study has described an effort to improve anticoagulation control across a health system. OBJECTIVE: To describe the results of an effort to improve anticoagulation control in the New England region of the Veterans Health Administration (VA). METHODS: Our intervention encompassed 8 VA sites managing warfarin for more than 5000 patients in New England (Veterans Integrated Service Network 1 [VISN 1]). We provided sites with a system to measure processes of care, along with targeted audit and feedback. We focused on processes of care associated with site-level anticoagulation control, including prompt follow-up after out-of-range international normalized ratio (INR) values, minimizing loss to follow-up, and use of guideline-concordant INR target ranges. We used a difference-in-differences (DID) model to examine changes in anticoagulation control, measured as percentage time in therapeutic range (TTR), as well as process measures and compared VISN 1 sites with 116 VA sites located outside VISN 1. RESULTS: VISN 1 sites improved on TTR, our main indicator of quality, from 66.4% to 69.2%, whereas sites outside VISN 1 improved from 65.9% to 66.4% (DID 2.3%, P < 0.001). Improvement in TTR correlated strongly with the extent of improvement on process-of-care measures, which varied widely across VISN 1 sites. CONCLUSIONS: A regional quality improvement initiative, using performance measurement with audit and feedback, improved TTR by 2.3% more than control sites, which is a clinically important difference. Improving relevant processes of care can improve outcomes for patients receiving warfarin.

A Systematic Review of Patient Safety Measures in Adult Primary Care.

Safety measure development has focused on inpatient care despite outpatient visits far outnumbering inpatient admissions. Some measures are clearly identified as outpatient safety measures when published, yet outcomes from quality improvement studies also may be useful measures. The authors conducted a systematic review of the literature to identify published articles detailing safety measures applicable to adult primary care. A total of 21 articles were identified, providing specifications for 182 safety measures. Each measure was classified into one of 6 outpatient safety dimensions: medication management, sentinel events, care coordination, procedures and treatment, laboratory testing and monitoring, and facility structures/resources. Compared to the multitude of available inpatient safety measures, the number of existing adult primary care measures is low. The measures identified by this systematic review may yield further insight into the breadth of safety events causing harm in primary care, while also identifying areas of patient safety in primary care that may be understudied.

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.

Measuring readmissions after surgery: do different methods tell the same story?

BACKGROUND: Readmission is widely used as a quality metric to assess hospital performance. However, different methods to calculate readmissions may produce various results, leading to differences in classification with respect to hospital performance. This study compared 2 commonly used approaches to measure surgical readmissions: the 30-day all-cause hospital-wide readmissions (HWRs) and the potentially preventable readmissions (PPRs). METHODS: We examined the correlation between hospitals' risk-adjusted HWR and PPR rates and whether there was agreement in categorizing hospital performance between these measures among 111 hospitals with inpatient surgical programs in the Veterans Health Administration. RESULTS: We found that hospitals' HWR and PPR rates were highly correlated (r = .85, P < .0001). The overall agreement between these 2 methods in categorizing hospital performance was 82% for all surgeries, 82% for colectomy, 84% for coronary bypass, and 87% for hip/knee replacement, respectively. CONCLUSIONS: Despite differences in methodologies, the HWR and the PPR measures provided relatively consistent perceptions of hospitals' performance on surgical readmissions.

Partnering With VA Stakeholders to Develop a Comprehensive Patient Safety Data Display: Lessons Learned From the Field.

Health care systems are increasingly burdened by the large numbers of safety measures currently being reported. Within the Veterans Administration (VA), most safety reporting occurs within organizational silos, with little involvement by the frontline users of these measures. To provide a more integrated picture of patient safety, the study team partnered with multiple VA stakeholders and engaged potential frontline users at 2 hospitals to develop a Guiding Patient Safety (GPS) tool. The GPS is currently in its fourth generation; once approval is obtained from senior leadership, implementation will begin. Stakeholders were enthusiastic about the GPS's user-friendly format, comprehensive content, and potential utility for improving safety. These findings suggest that stakeholder engagement is a critical first step in the development of tools that will more likely be used by frontline users. Policy makers and researchers may consider adopting this innovative partnered-research model in developing future national initiatives to deliver meaningful programs to frontline users.

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

BACKGROUND: In the USA, administrative data-based readmission rates such as the Centers for Medicare and Medicaid Services' all-cause readmission measures are used for public reporting and hospital payment penalties. To improve this measure and identify better quality improvement targets, 3M developed the Potentially Preventable Readmissions (PPRs) measure. It matches clinically related index admission and readmission diagnoses that may indicate readmissions resulting from admission- or post-discharge-related quality problems. OBJECTIVE: To examine whether PPR software-flagged pneumonia readmissions are associated with poorer quality of care. METHODS: Using a retrospective observational study design and Veterans Health Administration (VA) data, we identified pneumonia discharges associated with 30-day readmissions, and then flagged cases as PPR-yes or PPR-no using the PPR software. To assess quality of care, we abstracted electronic medical records of 100 random readmissions using a tool containing explicit care processes organised into admission work-up, in-hospital evaluation/treatment, discharge readiness and post-discharge period. We derived quality scores, scaled to a maximum of 25 per section (maximum total score=100) and compared cases by total and section-specific mean scores using t tests and effect size (ES) to characterise the clinical significance of findings. RESULTS: Our abstraction sample was selected from 11,278 pneumonia readmissions (readmission rate=16.5%) during 1 October 2005-30 September 2010; 77% were flagged as PPR-yes. Contrary to expectations, total and section mean quality scores were slightly higher, although non-significantly, among PPR-yes (N=77) versus PPR-no (N=23) cases (respective total scores, 71.2±8.7 vs 65.8±11.5, p=0.14); differences demonstrated ES >0.30 overall and for admission work-up and post-discharge period sections. CONCLUSIONS: Among VA pneumonia readmissions, PPR categorisation did not produce the expected quality of care findings. Either PPR-yes cases are not more preventable, or preventability assessment requires other data collection methods to capture poorly documented processes (eg, direct observation).

What is the value of adding Medicare data in estimating VA hospital readmission rates?

OBJECTIVE: To determine the effects of including diagnostic and utilization data from a secondary payer on readmission rates and hospital profiles. DATA SOURCES/STUDY SETTING: Veterans Health Administration (VA) and Medicare inpatient and outpatient administrative data for veterans discharged from 153 VA hospitals during FY 2008-2010 with a principal diagnosis of acute myocardial infarction, heart failure, or pneumonia. STUDY DESIGN: We estimated hospital-level risk-standardized readmission rates derived using VA data only. We then used data from both VA and Medicare to reestimate readmission rates and compared hospital profiles using two methods: Hospital Compare and the CMS implementation of the Hospital Readmissions Reduction Program (HRRP). DATA COLLECTION/EXTRACTION METHODS: Retrospective data analysis using VA hospital discharge and outpatient data matched with Medicare fee-for-service claims by scrambled Social Security numbers. PRINCIPAL FINDINGS: Less than 2 percent of hospitals in any cohort were classified discordantly by the Hospital Compare method when using VA-only compared with VA/Medicare data. In contrast, using the HRRP method, 13 percent of hospitals had differences in whether they were flagged as having excessive readmission rates in at least one cohort. CONCLUSIONS: Inclusion of secondary payer data may cause changes in hospital profiles, depending on the methodology used. An assessment of readmission rates should include, to the extent possible, all available information about patients' utilization of care.

Validity of ICD-9-CM codes for the identification of complications related to central venous catheterization.

Two complications of central venous catheterization (CVC), iatrogenic pneumothorax and central line-associated bloodstream infection (CLABSI), have dedicated International Classification of Diseases, Ninth Revision, Clinical Modification (ICD-9-CM) codes. Despite increasing use of ICD-9-CM codes for research and pay-for-performance purposes, their validity for detecting complications of CVC has not been established. Complications of CVCs placed between July 2010 and December 2011 were identified by ICD-9-CM codes in discharge records from a single hospital and compared with those revealed by medical record abstraction. The ICD-9-CM code for iatrogenic pneumothorax had a sensitivity of 66.7%, specificity of 100%, positive predictive value (PPV) of 100%, and negative predictive value (NPV) of 99.5%. The ICD-9-CM codes for CLABSI had a sensitivity of 33.3%, specificity of 99.0%, PPV of 28.6%, and NPV of 99.2%. The low sensitivity and variable PPV of ICD-9-CM codes for detection of complications of CVC raise concerns about their use for research or pay-for-performance purposes.

Using estimated true safety event rates versus flagged safety event rates: does it change hospital profiling and payment?

OBJECTIVE: To assess whether use of the AHRQ Patient Safety Indicator (PSI) composite measure versus modified composite measures leads to changes in hospital profiles and payments. DATA SOURCES/STUDY SETTING: Retrospective analysis of 2010 Veterans Health Administration discharge data. STUDY DESIGN: We used the AHRQ PSI software (v4.2) to obtain PSI-flagged events and composite scores for all 151 hospitals in the database (n = 517,814 hospitalizations). We compared the AHRQ PSI composite to two modified composites that estimated "true safety events" from previous chart abstraction findings: one with modified numerators based on the positive predictive value (PPV) of each PSI, and one with similarly modified numerators but whose denominators were based on the expected fraction of PSI-eligible cases that remained after removing those PSIs that were present-on-admission (POA). PRINCIPAL FINDINGS: Although a small percentage (5-6 percent) of hospitals changed outlier status based on modified PSI composites, some of these changes were substantial; 30 and 19 percent of hospitals changed ≥20 ranks after adjustment for PPVs and POA flags, respectively. We estimate that 33 percent of hospitals would see a change of at least 10 percent in performance payments. CONCLUSIONS: Changes in hospital profiles and payments would be substantial for some hospitals if the PSI composite score used weights reflecting the relative prevalence of true versus flagged events.

Comparison of the Agency for Healthcare Research and Quality Patient Safety Indicator Rates Among Veteran Dual Users.

This study compares rates of 11 Agency for Healthcare Research and Quality Patient Safety Indicators (PSIs) among 266 203 veteran dual users (ie, those with hospitalizations in both the Veterans Health Administration [VA] and the private sector through Medicare fee-for-service coverage) during 2002 to 2007. PSI risk-adjusted rates were calculated using the PSI software (version 3.1a). Rates of pressure ulcer, central venous catheter-related bloodstream infections, and postoperative sepsis, areas in which the VA has focused quality improvement efforts, were found to be significantly lower in the VA than in the private sector. VA had significantly higher rates for 7 of the remaining 8 PSIs, although the rates of only 2 PSIs (postoperative hemorrhage/hematoma and accidental puncture or laceration) remained higher in the VA after sensitivity analyses were conducted. A better understanding of system-level differences in coding practices and patient severity, poorly documented in administrative data, is needed before conclusions about differences in quality can be drawn.

Detecting adverse events in surgery: comparing events detected by the Veterans Health Administration Surgical Quality Improvement Program and the Patient Safety Indicators.

BACKGROUND: The Patient Safety Indicators (PSIs) use administrative data to screen for select adverse events (AEs). In this study, VA Surgical Quality Improvement Program (VASQIP) chart review data were used as the gold standard to measure the criterion validity of 5 surgical PSIs. Independent chart review was also used to determine reasons for PSI errors. METHODS: The sensitivity, specificity, and positive predictive value of PSI software version 4.1a were calculated among Veterans Health Administration hospitalizations (2003-2007) reviewed by VASQIP (n = 268,771). Nurses re-reviewed a sample of hospitalizations for which PSI and VASQIP AE detection disagreed. RESULTS: Sensitivities ranged from 31% to 68%, specificities from 99.1% to 99.8%, and positive predictive values from 31% to 72%. Reviewers found that coding errors accounted for some PSI-VASQIP disagreement; some disagreement was also the result of differences in AE definitions. CONCLUSIONS: These results suggest that the PSIs have moderate criterion validity; however, some surgical PSIs detect different AEs than VASQIP. Future research should explore using both methods to evaluate surgical quality.

Medical and surgical readmissions in the Veterans Health Administration: what proportion are related to the index hospitalization?

BACKGROUND: Readmissions are an attractive quality measure because they offer a broad view of quality beyond the index hospitalization. However, the extent to which medical or surgical readmissions reflect quality of care is largely unknown, because of the complexity of factors related to readmission. Identifying those readmissions that are clinically related to the index hospitalization is an important first step in closing this knowledge gap. OBJECTIVES: The aims of this study were to examine unplanned readmissions in the Veterans Health Administration, identify clinically related versus unrelated unplanned readmissions, and compare the leading reasons for unplanned readmission between medical and surgical discharges. METHODS: We classified 2,069,804 Veterans Health Administration hospital discharges (Fiscal Years 2003-2007) into medical/surgical index discharges with/without readmissions per their diagnosis-related groups. Our outcome variable was "all-cause" 30-day unplanned readmission. We compared medical and surgical unplanned readmissions (n=217,767) on demographics, clinical characteristics, and readmission reasons using descriptive statistics. RESULTS: Among all unplanned readmissions, 41.5% were identified as clinically related. Not surprisingly, heart failure (10.2%) and chronic obstructive pulmonary disease (6.5%) were the top 2 reasons for clinically related readmissions among medical discharges; postoperative complications (ie, complications of surgical procedures and medical care or complications of devices) accounted for 70.5% of clinically related readmissions among surgical discharges. CONCLUSIONS: Although almost 42% of unplanned readmissions were identified as clinically related, the majority of unplanned readmissions were unrelated to the index hospitalization. Quality improvement interventions targeted at processes of care associated with the index hospitalization are likely to be most effective in reducing clinically related readmissions. It is less clear how to reduce nonclinically related readmissions; these may involve broader factors than inpatient care.

Using AHRQ patient safety indicators to detect postdischarge adverse events in the Veterans Health Administration.

Patient safety indicators (PSIs) use inpatient administrative data to flag cases with potentially preventable adverse events (AEs) attributable to hospital care. This study explored how many AEs the PSIs identified in the 30 days post discharge. PSI software was run on Veterans Health Administration 2003-2007 administrative data for 10 recently validated PSIs. Among PSI-eligible index hospitalizations not flagged with an AE, this study evaluated how many AEs occurred within 1 to 14 and 15 to 30 days post discharge using inpatient and outpatient administrative data. Considering all PSI-eligible index hospitalizations, 11 141 postdischarge AEs were identified, compared with 40 578 inpatient-flagged AEs. More than 60% of postdischarge AEs were detected within 14 days of discharge. The majority of postdischarge AEs were decubitus ulcers and postoperative pulmonary embolisms or deep vein thromboses. Extending PSI algorithms to the postdischarge period may provide a more complete picture of hospital quality. Future work should use chart review to validate postdischarge PSI events.