Simulating complex patient populations with hierarchical learning effects to support methods development for post-market surveillance.

BACKGROUND: Validating new algorithms, such as methods to disentangle intrinsic treatment risk from risk associated with experiential learning of novel treatments, often requires knowing the ground truth for data characteristics under investigation. Since the ground truth is inaccessible in real world data, simulation studies using synthetic datasets that mimic complex clinical environments are essential. We describe and evaluate a generalizable framework for injecting hierarchical learning effects within a robust data generation process that incorporates the magnitude of intrinsic risk and accounts for known critical elements in clinical data relationships. METHODS: We present a multi-step data generating process with customizable options and flexible modules to support a variety of simulation requirements. Synthetic patients with nonlinear and correlated features are assigned to provider and institution case series. The probability of treatment and outcome assignment are associated with patient features based on user definitions. Risk due to experiential learning by providers and/or institutions when novel treatments are introduced is injected at various speeds and magnitudes. To further reflect real-world complexity, users can request missing values and omitted variables. We illustrate an implementation of our method in a case study using MIMIC-III data for reference patient feature distributions. RESULTS: Realized data characteristics in the simulated data reflected specified values. Apparent deviations in treatment effects and feature distributions, though not statistically significant, were most common in small datasets (n < 3000) and attributable to random noise and variability in estimating realized values in small samples. When learning effects were specified, synthetic datasets exhibited changes in the probability of an adverse outcomes as cases accrued for the treatment group impacted by learning and stable probabilities as cases accrued for the treatment group not affected by learning. CONCLUSIONS: Our framework extends clinical data simulation techniques beyond generation of patient features to incorporate hierarchical learning effects. This enables the complex simulation studies required to develop and rigorously test algorithms developed to disentangle treatment safety signals from the effects of experiential learning. By supporting such efforts, this work can help identify training opportunities, avoid unwarranted restriction of access to medical advances, and hasten treatment improvements.

Acute kidney injury is a risk factor for subsequent proteinuria.

Acute kidney injury (AKI) is associated with subsequent chronic kidney disease (CKD), but the mechanism is unclear. To clarify this, we examined the association of AKI and new-onset or worsening proteinuria during the 12 months following hospitalization in a national retrospective cohort of United States Veterans hospitalized between 2004-2012. Patients with and without AKI were matched using baseline demographics, comorbidities, proteinuria, estimated glomerular filtration rate, blood pressure, angiotensin-converting enzyme inhibitor or angiotensin II receptor blocker (ACEI/ARB) use, and inpatient exposures linked to AKI. The distribution of proteinuria over one year post-discharge in the matched cohort was compared using inverse probability sampling weights. Subgroup analyses were based on diabetes, pre-admission ACEI/ARB use, and AKI severity. Among the 90,614 matched AKI and non-AKI pairs, the median estimated glomerular filtration rate was 62 mL/min/1.73m2. The prevalence of diabetes and hypertension were 48% and 78%, respectively. The odds of having one plus or greater dipstick proteinuria was significantly higher during each month of follow-up in patients with AKI than in patients without AKI (odds ratio range 1.20-1.39). Odds were higher in patients with Stage II or III AKI (odds ratios 1.32-1.81) than in Stage I AKI (odds ratios 1.18-1.32), using non-AKI as the reference group. Results were consistent regardless of diabetes status or baseline ACEI/ARB use. Thus, AKI is a risk factor for incident or worsening proteinuria, suggesting a possible mechanism linking AKI and future CKD. The type of proteinuria, physiology, and clinical significance warrant further study as a potentially modifiable risk factor in the pathway from AKI to CKD.

Acute Kidney Injury and Risk of Incident Heart Failure Among US Veterans.

BACKGROUND: Acute kidney injury (AKI) is common and associated with poor outcomes. Heart failure is a leading cause of cardiovascular disease among patients with chronic kidney disease. The relationship between AKI and heart failure remains unknown and may identify a novel mechanistic link between kidney and cardiovascular disease. STUDY DESIGN: Observational study. SETTING & PARTICIPANTS: We studied a national cohort of 300,868 hospitalized US veterans (2004-2011) without a history of heart failure. PREDICTOR: AKI was the predictor and was defined as a 0.3-mg/dL or 50% increase in serum creatinine concentration from baseline to the peak hospital value. Patients with and without AKI were matched (1:1) on 28 in- and outpatient covariates using optimal Mahalanobis distance matching. OUTCOMES: Incident heart failure was defined as 1 or more hospitalization or 2 or more outpatient visits with a diagnosis of heart failure within 2 years through 2013. RESULTS: There were 150,434 matched pairs in the study. Patients with and without AKI during the index hospitalization were well matched, with a median preadmission estimated glomerular filtration rate of 69mL/min/1.73m2. The overall incidence rate of heart failure was 27.8 (95% CI, 19.3-39.9) per 1,000 person-years. The incidence rate was higher in those with compared with those without AKI: 30.8 (95% CI, 21.8-43.5) and 24.9 (95% CI, 16.9-36.5) per 1,000 person-years, respectively. In multivariable models, AKI was associated with 23% increased risk for incident heart failure (HR, 1.23; 95% CI, 1.19-1.27). LIMITATIONS: Study population was primarily men, reflecting patients seen at Veterans Affairs hospitals. CONCLUSIONS: AKI is an independent risk factor for incident heart failure. Future studies to identify underlying mechanisms and modifiable risk factors are needed.

The Cost of ICU Delirium and Coma in the Intensive Care Unit Patient.

RATIONALE: Intensive care unit (ICU) delirium is highly prevalent and a potentially avoidable hospital complication. The current cost of ICU delirium is unknown. OBJECTIVES: To specify the association between the daily occurrence of delirium in the ICU with costs of ICU care accounting for time-varying illness severity and death. RESEARCH DESIGN: We performed a prospective cohort study within medical and surgical ICUs in a large academic medical center. SUBJECTS: We analyzed critically ill patients (N=479) with respiratory failure and/or shock. MEASURES: Covariates included baseline factors (age, insurance, cognitive impairment, comorbidities, Acute Physiology and Chronic Health Evaluation II Score) and time-varying factors (sequential organ failure assessment score, mechanical ventilation, and severe sepsis). The primary analysis used a novel 3-stage regression method: first, estimation of the cumulative cost of delirium over 30 ICU days and then costs separated into those attributable to increased resource utilization among survivors and those that were avoided on the account of delirium's association with early mortality in the ICU. RESULTS: The patient-level 30-day cumulative cost of ICU delirium attributable to increased resource utilization was $17,838 (95% confidence interval, $11,132-$23,497). A combination of professional, dialysis, and bed costs accounted for the largest percentage of the incremental costs associated with ICU delirium. The 30-day cumulative incremental costs of ICU delirium that were avoided due to delirium-associated early mortality was $4654 (95% confidence interval, $2056-7869). CONCLUSIONS: Delirium is associated with substantial costs after accounting for time-varying illness severity and could be 20% higher (∼$22,500) if not for its association with early ICU mortality.

A Multimodal Intervention Improves Postanesthesia Care Unit Handovers.

BACKGROUND: Failures of communication are a major contributor to perioperative adverse events. Transitions of care may be particularly vulnerable. We sought to improve postoperative handovers. METHODS: We introduced a multimodal intervention in an adult and a pediatric postanesthesia care unit (PACU) to improve postoperative handovers between anesthesia providers (APs) and PACU registered nurses (RNs). The intervention included a standardized electronic handover report form, a didactic webinar, mandatory simulation training focused on improving interprofessional communication, and post-training performance feedback. Trained, blinded nurse observers scored PACU handovers during 17 months using a structured tool consisting of 8 subscales and a global score (1-5 scale). Multivariate logistic regression assessed the effect of the intervention on the proportion of observed handovers receiving a global effectiveness rating of ≥3. RESULTS: Four hundred fifty-two clinicians received the simulation-based training, and 981 handovers were observed and rated. In the adult PACU, the estimated percentages of acceptable handovers (global ratings ≥3) among AP-RN pairs, where neither received simulation-based training (untrained dyads), was 3% (95% confidence interval, 1%-11%) at day 0, 10% (5%-19%) at training initiation (day 40), and 57% (33%-78%) at 1-year post-training initiation (day 405). For AP-RN pairs where at least one received the simulation-based training (trained dyads), these percentages were estimated to be 18% (11%-28%) and 68% (57%-76%) on days 40 and 405, respectively. The percentage of acceptable handovers was significantly greater on day 405 than it was on day 40 for both untrained (P < 0.001) and trained dyads (P < 0.001). Similar patterns were observed in the pediatric PACU. Three years later, the unadjusted estimate of the probability of an acceptable handover was 87% (72%-95%) in the adult PACU and 56% (40%-72%) in the pediatric PACU. CONCLUSIONS: A multimodal intervention substantially improved interprofessional PACU handovers, including those by clinicians who had not undergone formal simulation training. An effect appeared to be present >3 years later.

Timeliness of interfacility transfer for ED patients with ST-elevation myocardial infarction.

OBJECTIVES: Most US hospitals lack primary percutaneous coronary intervention (PCI) capabilities to treat patients with ST-elevation myocardial infarction (STEMI) necessitating transfer to PCI-capable centers. Transferred patients rarely meet the 120-minute benchmark for timely reperfusion, and referring emergency departments (EDs) are a major source of preventable delays. We sought to use more granular data at transferring EDs to describe the variability in length of stay at referring EDs. METHODS: We retrospectively analyzed a secondary data set used for quality improvement for patients with STEMI transferred to a single PCI center between 2008 and 2012. We conducted a descriptive analysis of the total time spent at each referring ED (door-in-door-out [DIDO] interval), periods that comprised DIDO (door to electrocardiogram [EKG], EKG-to-PCI activation, and PCI activation to exit), and the relationship of each period with overall time to reperfusion (medical contact-to-balloon [MCTB] interval). RESULTS: We identified 41 EDs that transferred 620 patients between 2008 and 2012. Median MCTB was 135 minutes (interquartile range [IQR] 114,172). Median overall ED DIDO was 74 minutes (IQR 56,103) and was composed of door to EKG, 5 minutes (IQR 2,11); EKG-to-PCI activation, 18 minutes (IQR 7,37); and PCI activation to exit, 44 minutes (IQR 34,56). Door-in door-out accounted for the largest proportion (60%) of overall MCTB and had the largest variability (coefficient of variability, 1.37) of these intervals. CONCLUSIONS: In this cohort of transferring EDs, we found high variability and substantial delays after EKG performance for patients with STEMI. Factors influencing ED decision making and transportation coordination after PCI activation are a potential target for intervention to improve the timeliness of reperfusion in patients with STEMI.

Development and evaluation of RapTAT: a machine learning system for concept mapping of phrases from medical narratives.

Rapid, automated determination of the mapping of free text phrases to pre-defined concepts could assist in the annotation of clinical notes and increase the speed of natural language processing systems. The aim of this study was to design and evaluate a token-order-specific naïve Bayes-based machine learning system (RapTAT) to predict associations between phrases and concepts. Performance was assessed using a reference standard generated from 2860 VA discharge summaries containing 567,520 phrases that had been mapped to 12,056 distinct Systematized Nomenclature of Medicine - Clinical Terms (SNOMED CT) concepts by the MCVS natural language processing system. It was also assessed on the manually annotated, 2010 i2b2 challenge data. Performance was established with regard to precision, recall, and F-measure for each of the concepts within the VA documents using bootstrapping. Within that corpus, concepts identified by MCVS were broadly distributed throughout SNOMED CT, and the token-order-specific language model achieved better performance based on precision, recall, and F-measure (0.95±0.15, 0.96±0.16, and 0.95±0.16, respectively; mean±SD) than the bag-of-words based, naïve Bayes model (0.64±0.45, 0.61±0.46, and 0.60±0.45, respectively) that has previously been used for concept mapping. Precision, recall, and F-measure on the i2b2 test set were 92.9%, 85.9%, and 89.2% respectively, using the token-order-specific model. RapTAT required just 7.2ms to map all phrases within a single discharge summary, and mapping rate did not decrease as the number of processed documents increased. The high performance attained by the tool in terms of both accuracy and speed was encouraging, and the mapping rate should be sufficient to support near-real-time, interactive annotation of medical narratives. These results demonstrate the feasibility of rapidly and accurately mapping phrases to a wide range of medical concepts based on a token-order-specific naïve Bayes model and machine learning.

Exploring the frontier of electronic health record surveillance: the case of postoperative complications.

BACKGROUND: The aim of this study was to build electronic algorithms using a combination of structured data and natural language processing (NLP) of text notes for potential safety surveillance of 9 postoperative complications. METHODS: Postoperative complications from 6 medical centers in the Southeastern United States were obtained from the Veterans Affairs Surgical Quality Improvement Program (VASQIP) registry. Development and test datasets were constructed using stratification by facility and date of procedure for patients with and without complications. Algorithms were developed from VASQIP outcome definitions using NLP-coded concepts, regular expressions, and structured data. The VASQIP nurse reviewer served as the reference standard for evaluating sensitivity and specificity. The algorithms were designed in the development and evaluated in the test dataset. RESULTS: Sensitivity and specificity in the test set were 85% and 92% for acute renal failure, 80% and 93% for sepsis, 56% and 94% for deep vein thrombosis, 80% and 97% for pulmonary embolism, 88% and 89% for acute myocardial infarction, 88% and 92% for cardiac arrest, 80% and 90% for pneumonia, 95% and 80% for urinary tract infection, and 77% and 63% for wound infection, respectively. A third of the complications occurred outside of the hospital setting. CONCLUSIONS: Computer algorithms on data extracted from the electronic health record produced respectable sensitivity and specificity across a large sample of patients seen in 6 different medical centers. This study demonstrates the utility of combining NLP with structured data for mining the information contained within the electronic health record.

The National Neurosurgery Quality and Outcomes Database (N2QOD): general overview and pilot-year project description.

Given the unsustainable costs of US health care, universal agreement exists among payers, regulatory agencies, and other health care stakeholders that reform must include substantial improvements in the quality, effectiveness, and value of health care delivery. The Institute of Medicine and the American Recovery and Reinvestment Act of 2009 have called for the establishment of prospective registries to capture patient-centered data from real-world practice as a high priority to guide evidence-based reform. As a result, the American Association of Neurological Surgeons launched the National Neurosurgery Quality and Outcomes Database (N(2)QOD) and began enrolling patients in March 2012 into its initial pilot project: a web-based lumbar spine module. As a nationwide, prospective longitudinal registry utilizing patient reported outcome instruments, the N(2)QOD lumbar spine surgery pilot aims to systematically measure and aggregate surgical safety and 1-year postoperative outcome data from approximately 30 neurosurgical practices across the US with the primary aim of demonstrating the feasibility and validity of standardized 1-year outcome measurement from everyday real-world practice. At the end of the pilot year, 1) risk-adjusted modeling will be developed for the safety, quality, and effectiveness of lumbar surgical care (morbidity, readmission, improvements in pain, disability, quality of life, and return to work); 2) data integrity and validation will be demonstrated via internal quality control analyses and auditing, and 3) the feasibility of obtaining a high level of follow-up (~80%) of nationwide 1-year outcome measurement will be established. N(2)QOD will use only prospective clinical data, will avoid the use of administrative data proxies, and will rely on neurosurgically relevant risk factors for risk adjustment. Once national benchmarks of quality and effectiveness are accurately established and validated utilizing practice-based data extractors in the pilot year, N(2)QOD aims to introduce non-full-time employee (FTE)-dependent methodologies such as electronic medical record auto-extraction. N(2)QOD's non-FTE-dependent methodologies can then be validated against practice-based data extractor-derived measures of safety and effectiveness with the aim of more rapid expansion into the majority of US practice groups. The general overview, methods, and registry design of the N(2)QOD pilot year (lumbar module) are presented here.

Outcome science in practice: an overview and initial experience at the Vanderbilt Spine Center.

In terms of policy, research, quality improvement, and practice-based learning, there are essential principles--namely, quality, effectiveness, and value of care--needed to navigate changes in the current and future US health care environment. Patient-centered outcome measurement lies at the core of all 3 principles. Multiple measures of disease-specific disability, generic health-related quality of life, and preference-based health state have been introduced to quantify disease impact and define effectiveness of care. This paper reviews the basic principles of patient outcome measurement and commonly used outcome instruments. The authors provide examples of how utilization of outcome measurement tools in everyday neurosurgical practice can facilitate practice-based learning, quality improvement, and real-world comparative effectiveness research, as well as promote the value of neurosurgical care.

Outpatient nephrology referral rates after acute kidney injury.

AKI associates with an increased risk for the development and progression of CKD and mortality. Processes of care after an episode of AKI are not well described. Here, we examined the likelihood of nephrology referral among survivors of AKI at risk for subsequent decline in kidney function in a US Department of Veterans Affairs database. We identified 3929 survivors of AKI hospitalized between January 2003 and December 2008 who had an estimated GFR (eGFR) <60 ml/min per 1.73 m(2) 30 days after peak injury. We analyzed time to referral considering improvement in kidney function (eGFR ≥60 ml/min per 1.73 m(2)), dialysis initiation, and death as competing risks over a 12-month surveillance period. Median age was 73 years (interquartile range, 62-79 years) and the prevalence of preadmission kidney dysfunction (baseline eGFR <60 ml/min per 1.73 m(2)) was 60%. Overall mortality during the surveillance period was 22%. The cumulative incidence of nephrology referral before dying, initiating dialysis, or experiencing an improvement in kidney function was 8.5% (95% confidence interval, 7.6-9.4). Severity of AKI did not affect referral rates. These data demonstrate that a minority of at-risk survivors are referred for nephrology care after an episode of AKI. Determining how to best identify survivors of AKI who are at highest risk for complications and progression of CKD could facilitate early nephrology-based interventions.

Impact of evidence-based standardized assessment on the disability clinical interview for diagnosis of service-connected PTSD: a cluster-randomized trial.

Posttraumatic stress disorder (PTSD) is one of the fastest growing compensated medical conditions. The present study compared usual disability examiner practices for PTSD with a standardized assessment that incorporates evidence-based assessments. The design was a multicenter, cluster randomized, parallel-group study involving 33 clinical examiners and 384 veterans at 6 Veterans Affairs medical centers. The standardized group incorporated the Clinician Administered PTSD Scale and the World Health Organization Disability Assessment Schedule-II into their assessment interview. The main outcome measures were completeness and accuracy of PTSD diagnosis and completeness of functional assessment. The standardized assessments were 85% complete for diagnosis compared to 30% for nonstandardized assessments (p < .001), and, for functional impairment, 76% versus 3% (p < .001). The findings demonstrate that the quality of PTSD disability examination would be improved by using evidence-based assessment.

Variation in practices and attitudes of clinicians assessing PTSD-related disability among veterans.

One hundred thirty-eight Veterans Affairs mental health professionals completed a 128-item Posttraumatic Stress Disorder (PTSD) Practice Inventory that asked about their practices and attitudes related to disability assessment of PTSD. Results indicate strikingly wide variation in the attitudes and practices of clinicians conducting disability assessments for PTSD. In a high percentage of cases, these attitudes and practices conflict with best-practice guidelines. Specifically, 59% of clinicians reported rarely or never using testing, and only 17% indicated routinely using standardized clinical interviews. Less than 1% of respondents reported using functional assessment scales.

Patient centered primary care is associated with patient hypertension medication adherence.

There is increasing evidence that patient centered care, including communication skills, is an essential component to chronic illness care. Our aim was to evaluate patient centered primary care as a determinant of medication adherence. We mailed 1,341 veterans with hypertension the Short Form Primary Care Assessment Survey (PCAS) which measures elements of patient centered primary care. We prospectively collected each patient's antihypertensive medication adherence for 6 months. Patients were characterized as adherent if they had medication for >80%. 654 surveys were returned (50.7%); and 499 patients with complete data were analyzed. Antihypertensive adherence increased as scores in patient centered care increased [RR 3.18 (95% CI 1.44, 16.23) bootstrap 5000 resamples] for PCAS score of 4.5 (highest quartile) versus 1.5 (lowest quartile). Future research is needed to determine if improving patient centered care, particularly communication skills, could lead to improvements in health related behaviors such as medication adherence and health outcomes.

Automated identification of postoperative complications within an electronic medical record using natural language processing.

CONTEXT: Currently most automated methods to identify patient safety occurrences rely on administrative data codes; however, free-text searches of electronic medical records could represent an additional surveillance approach. OBJECTIVE: To evaluate a natural language processing search-approach to identify postoperative surgical complications within a comprehensive electronic medical record. DESIGN, SETTING, AND PATIENTS: Cross-sectional study involving 2974 patients undergoing inpatient surgical procedures at 6 Veterans Health Administration (VHA) medical centers from 1999 to 2006. MAIN OUTCOME MEASURES: Postoperative occurrences of acute renal failure requiring dialysis, deep vein thrombosis, pulmonary embolism, sepsis, pneumonia, or myocardial infarction identified through medical record review as part of the VA Surgical Quality Improvement Program. We determined the sensitivity and specificity of the natural language processing approach to identify these complications and compared its performance with patient safety indicators that use discharge coding information. RESULTS: The proportion of postoperative events for each sample was 2% (39 of 1924) for acute renal failure requiring dialysis, 0.7% (18 of 2327) for pulmonary embolism, 1% (29 of 2327) for deep vein thrombosis, 7% (61 of 866) for sepsis, 16% (222 of 1405) for pneumonia, and 2% (35 of 1822) for myocardial infarction. Natural language processing correctly identified 82% (95% confidence interval [CI], 67%-91%) of acute renal failure cases compared with 38% (95% CI, 25%-54%) for patient safety indicators. Similar results were obtained for venous thromboembolism (59%, 95% CI, 44%-72% vs 46%, 95% CI, 32%-60%), pneumonia (64%, 95% CI, 58%-70% vs 5%, 95% CI, 3%-9%), sepsis (89%, 95% CI, 78%-94% vs 34%, 95% CI, 24%-47%), and postoperative myocardial infarction (91%, 95% CI, 78%-97%) vs 89%, 95% CI, 74%-96%). Both natural language processing and patient safety indicators were highly specific for these diagnoses. CONCLUSION: Among patients undergoing inpatient surgical procedures at VA medical centers, natural language processing analysis of electronic medical records to identify postoperative complications had higher sensitivity and lower specificity compared with patient safety indicators based on discharge coding.

Measuring and comparing safety climate in intensive care units.

BACKGROUND: Learning about the factors that influence safety climate and improving the methods for assessing relative performance among hospital or units would improve decision-making for clinical improvement. OBJECTIVES: To measure safety climate in intensive care units (ICU) owned by a large for-profit integrated health delivery systems; identify specific provider, ICU, and hospital factors that influence safety climate; and improve the reporting of safety climate data for comparison and benchmarking. RESEARCH DESIGN: We administered the Safety Attitudes Questionnaire (SAQ) to clinicians, staff, and administrators in 110 ICUs from 61 hospitals. SUBJECTS: A total of 1502 surveys (43% response) from physicians, nurses, respiratory therapists, pharmacists, mangers, and other ancillary providers. MEASURES: The survey measured safety climate across 6 domains: teamwork climate; safety climate; perceptions of management; job satisfaction; working conditions; and stress recognition. Percentage of positive scores, mean scores, unadjusted random effects, and covariate-adjusted random effect were used to rank ICU performance. RESULTS: The cohort was characterized by a positive safety climate. Respondents scored perceptions of management and working conditions significantly lower than the other domains of safety climate. Respondent job type was significantly associated with safety climate and domain scores. There was modest agreement between ranking methodologies using raw scores and random effects. CONCLUSIONS: The relative proportion of job type must be considered before comparing safety climate results across organizational units. Ranking methodologies based on raw scores and random effects are viable for feedback reports. The use of covariate-adjusted random effects is recommended for hospital decision-making.

Are posttraumatic stress disorder mental health terms found in SNOMED-CT medical terminology.

The authors sought to evaluate how well the Systematized Nomenclature of Medicine-Clinical Terms (SNOMED-CT) controlled vocabulary represents terms commonly used clinically when documenting posttraumatic stress disorder (PTSD). A list was constructed based on the PTSD criteria in the Diagnostic and Statistical Manual of Mental Disorders, Fourth Edition (DSM-IV; American Psychiatric Association, 1994), symptom assessment instruments, and publications. Although two teams mapping the terms to SNOMED-CT differed in their approach, the consensus mapping accounted for 91% of the 153 PTSD terms. They found that the words used by clinicians in describing PTSD symptoms are represented in SNOMED-CT. These results can be used to codify mental health text reports for health information technology applications such as automated chart abstraction, algorithms for identifying documentation of symptoms representing PTSD in clinical notes, and clinical decision support.

Medication Discrepancies in Older Veterans Receiving Home Healthcare.

In a prospective cohort study of Veterans and community health nurses, we enrolled hospitalized older Veterans referred to home care for skilled nursing and/or physical or occupational therapy for posthospitalization care. We assessed preadmission activities of daily living and instrumental activities of daily living, health literacy, numeracy, and cognition. Postdischarge phone calls identified medication errors and medication reconciliation efforts by home healthcare clinicians. Veterans Administration-based community health nurses completed surveys about content and timing of postdischarge interactions with home healthcare clinicians. We determined the types and frequency of medication errors among older Veterans receiving home healthcare, patient-provider communication patterns in this setting, and patient characteristics affecting medication error rates. Most Veterans (24/30, 80%) had at least one discordant medication, and only one noted that errors were identified and resolved. Veterans were asked about medications in the home healthcare setting, but far fewer were questioned about medication-taking details, adherence, and as-needed or nonoral medications. Higher numeracy was associated with fewer errors. Veterans Administration community health nurses reported contact by home healthcare clinicians in 41% of cases (7/17). Given the high rate of medication errors discovered, future work should focus on implementing best practices for medication review in this setting, as well as documenting barriers/facilitators of patient-provider communication.

Effect of short call admission on length of stay and quality of care for acute decompensated heart failure.

BACKGROUND: In response to residency work hour restrictions, programs restructured call schedules, increasing the use of short call (daytime admitting teams). Few data exist on the effect of short call on quality of patient care. Our objective was to examine the effect of short call admission on length of stay and quality of care for patients with acute decompensated heart failure. METHODS AND RESULTS: We conducted a retrospective cohort study of 218 patients admitted with acute decompensated heart failure to the Nashville VA Medical Center between July 1, 2003, and June 30, 2005. The primary exposure was short call, and the primary outcome was length of stay. The secondary outcomes--diuretic dosing, weight monitoring, and hospital complications--were determined through a combination of administrative data and chart review. Patients admitted to short call had a longer median length of stay than patients admitted to long call (5.2 days [25% to 75%, 3.2 to 8 days] versus 3.9 days [interquartile range, 2.7 to 6.5 days]; P=0.0004). After adjustment for covariates, short call had a 44% increase in length of stay (95% CI, 15 to 80) compared with long call. Short call patients received fewer diuretic doses in the first 24 hours of hospitalization (1.80 versus 2.12; P=0.014) and had a longer median time to the second dose of loop diuretics compared with long call patients (17.9 hours versus 16.2 hours; P=0.044). CONCLUSIONS: Admission to short call is predictive of increased length of stay, a decreased number of diuretic doses, and delays in the timing of diuretics among patients with acute decompensated heart failure. Additional studies are needed to clarify the impact of short call admission on inpatient quality of care.

Delirium and sedation in the intensive care unit: survey of behaviors and attitudes of 1384 healthcare professionals.

OBJECTIVE: A 2001 survey found that most healthcare professionals considered intensive care unit (ICU) delirium as a serious problem, but only 16% used a validated delirium screening tool. Our objective was to assess beliefs and practices regarding ICU delirium and sedation management. DESIGN AND SETTING: Between October 2006 and May 2007, a survey was distributed to ICU practitioners in 41 North American hospitals, seven international critical care meetings and courses, and the American Thoracic Society e-mail database. STUDY PARTICIPANTS: A convenience sample of 1384 healthcare professionals including 970 physicians, 322 nurses, 23 respiratory care practitioners, 26 pharmacists, 18 nurse practitioners and physicians' assistants, and 25 others. RESULTS: A majority [59% (766 of 1300)] estimated that more than one in four adult mechanically ventilated patients experience delirium. More than half [59% (774 of 1302)] screen for delirium, with 33% of those respondents (258 of 774) using a specific screening tool. A majority of respondents use a sedation protocol, but 29% (396 of 1355) still do not. A majority (76%, 990 of 1309) has a written policy on spontaneous awakening trials (SATs), but the minority of respondents (44%, 446 of 1019) practice spontaneous awakening trials on more than half of ICU days. CONCLUSIONS: Delirium is considered a serious problem by a majority of healthcare professionals, and the percent of practitioners using a specific screening tool has increased since the last published survey data. Although most respondents have adopted specific sedation protocols and have an approved approach to stopping sedation daily, few report even modest compliance with daily cessation of sedation.