Pragmatic Trial of Hospitalization Rate in Chronic Kidney Disease.

BACKGROUND: Despite the availability of effective therapies for patients with chronic kidney disease, type 2 diabetes, and hypertension (the kidney-dysfunction triad), the results of large-scale trials examining the implementation of guideline-directed therapy to reduce the risk of death and complications in this population are lacking. METHODS: In this open-label, cluster-randomized trial, we assigned 11,182 patients with the kidney-dysfunction triad who were being treated at 141 primary care clinics either to receive an intervention that used a personalized algorithm (based on the patient's electronic health record [EHR]) to identify patients and practice facilitators to assist providers in delivering guideline-based interventions or to receive usual care. The primary outcome was hospitalization for any cause at 1 year. Secondary outcomes included emergency department visits, readmissions, cardiovascular events, dialysis, and death. RESULTS: We assigned 71 practices (enrolling 5690 patients) to the intervention group and 70 practices (enrolling 5492 patients) to the usual-care group. The hospitalization rate at 1 year was 20.7% (95% confidence interval [CI], 19.7 to 21.8) in the intervention group and 21.1% (95% CI, 20.1 to 22.2) in the usual-care group (between-group difference, 0.4 percentage points; P = 0.58). The risks of emergency department visits, readmissions, cardiovascular events, dialysis, or death from any cause were similar in the two groups. The risk of adverse events was also similar in the trial groups, except for acute kidney injury, which was observed in more patients in the intervention group (12.7% vs. 11.3%). CONCLUSIONS: In this pragmatic trial involving patients with the triad of chronic kidney disease, type 2 diabetes, and hypertension, the use of an EHR-based algorithm and practice facilitators embedded in primary care clinics did not translate into reduced hospitalization at 1 year. (Funded by the National Institutes of Health and others; ICD-Pieces ClinicalTrials.gov number, NCT02587936.).

Vital Signs Are Still Vital: Instability on Discharge and the Risk of Post-Discharge Adverse Outcomes.

BACKGROUND: Vital sign instability on discharge could be a clinically objective means of assessing readiness and safety for discharge; however, the association between vital sign instability on discharge and post-hospital outcomes is unclear. OBJECTIVE: To assess the association between vital sign instability at hospital discharge and post-discharge adverse outcomes. DESIGN: Multi-center observational cohort study using electronic health record data. Abnormalities in temperature, heart rate, blood pressure, respiratory rate, and oxygen saturation were assessed within 24 hours of discharge. We used logistic regression adjusted for predictors of 30-day death and readmission. PARTICIPANTS: Adults (≥18 years) with a hospitalization to any medicine service in 2009-2010 at six hospitals (safety-net, community, teaching, and non-teaching) in north Texas. MAIN MEASURES: Death or non-elective readmission within 30 days after discharge. KEY RESULTS: Of 32,835 individuals, 18.7 % were discharged with one or more vital sign instabilities. Overall, 12.8 % of individuals with no instabilities on discharge died or were readmitted, compared to 16.9 % with one instability, 21.2 % with two instabilities, and 26.0 % with three or more instabilities (p < 0.001). The presence of any (≥1) instability was associated with higher risk-adjusted odds of either death or readmission (AOR 1.36, 95 % CI 1.26-1.48), and was more strongly associated with death (AOR 2.31, 95 % CI 1.91-2.79). Individuals with three or more instabilities had nearly fourfold increased odds of death (AOR 3.91, 95 % CI 1.69-9.06) and increased odds of 30-day readmission (AOR 1.36, 95 % 0.81-2.30) compared to individuals with no instabilities. Having two or more vital sign instabilities at discharge had a positive predictive value of 22 % and positive likelihood ratio of 1.8 for 30-day death or readmission. CONCLUSIONS: Vital sign instability on discharge is associated with increased risk-adjusted rates of 30-day mortality and readmission. These simple vital sign criteria could be used to assess safety for discharge, and to reduce 30-day mortality and readmissions.

Consensus Statement on Electronic Health Predictive Analytics: A Guiding Framework to Address Challenges.

CONTEXT: The recent explosion in available electronic health record (EHR) data is motivating a rapid expansion of electronic health care predictive analytic (e-HPA) applications, defined as the use of electronic algorithms that forecast clinical events in real time with the intent to improve patient outcomes and reduce costs. There is an urgent need for a systematic framework to guide the development and application of e-HPA to ensure that the field develops in a scientifically sound, ethical, and efficient manner. OBJECTIVES: Building upon earlier frameworks of model development and utilization, we identify the emerging opportunities and challenges of e-HPA, propose a framework that enables us to realize these opportunities, address these challenges, and motivate e-HPA stakeholders to both adopt and continuously refine the framework as the applications of e-HPA emerge. METHODS: To achieve these objectives, 17 experts with diverse expertise including methodology, ethics, legal, regulation, and health care delivery systems were assembled to identify emerging opportunities and challenges of e-HPA and to propose a framework to guide the development and application of e-HPA. FINDINGS: The framework proposed by the panel includes three key domains where e-HPA differs qualitatively from earlier generations of models and algorithms (Data Barriers, Transparency, and ETHICS) and areas where current frameworks are insufficient to address the emerging opportunities and challenges of e-HPA (Regulation and Certification; and Education and Training). The following list of recommendations summarizes the key points of the framework: Data Barriers: Establish mechanisms within the scientific community to support data sharing for predictive model development and testing.Transparency: Set standards around e-HPA validation based on principles of scientific transparency and reproducibility. ETHICS: Develop both individual-centered and society-centered risk-benefit approaches to evaluate e-HPA.Regulation and Certification: Construct a self-regulation and certification framework within e-HPA.Education and Training: Make significant changes to medical, nursing, and paraprofessional curricula by including training for understanding, evaluating, and utilizing predictive models.

Predicting all-cause readmissions using electronic health record data from the entire hospitalization: Model development and comparison.

BACKGROUND: Incorporating clinical information from the full hospital course may improve prediction of 30-day readmissions. OBJECTIVE: To develop an all-cause readmissions risk-prediction model incorporating electronic health record (EHR) data from the full hospital stay, and to compare "full-stay" model performance to a "first day" and 2 other validated models, LACE (includes Length of stay, Acute [nonelective] admission status, Charlson Comorbidity Index, and Emergency department visits in the past year), and HOSPITAL (includes Hemoglobin at discharge, discharge from Oncology service, Sodium level at discharge, Procedure during index hospitalization, Index hospitalization Type [nonelective], number of Admissions in the past year, and Length of stay). DESIGN: Observational cohort study. SUBJECTS: All medicine discharges between November 2009 and October 2010 from 6 hospitals in North Texas, including safety net, teaching, and nonteaching sites. MEASURES: Thirty-day nonelective readmissions were ascertained from 75 regional hospitals. RESULTS: Among 32,922 admissions (validation = 16,430), 12.7% were readmitted. In addition to many first-day factors, we identified hospital-acquired Clostridium difficile infection (adjusted odds ratio [AOR]: 2.03, 95% confidence interval [CI]: 1.18-3.48), vital sign instability on discharge (AOR: 1.25, 95% CI: 1.15-1.36), hyponatremia on discharge (AOR: 1.34, 95% CI: 1.18-1.51), and length of stay (AOR: 1.06, 95% CI: 1.04-1.07) as significant predictors. The full-stay model had better discrimination than other models though the improvement was modest (C statistic 0.69 vs 0.64-0.67). It was also modestly better in identifying patients at highest risk for readmission (likelihood ratio +2.4 vs. 1.8-2.1) and in reclassifying individuals (net reclassification index 0.02-0.06). CONCLUSIONS: Incorporating clinically granular EHR data from the full hospital stay modestly improves prediction of 30-day readmissions. Given limited improvement in prediction despite incorporation of data on hospital complications, clinical instabilities, and trajectory, our findings suggest that many factors influencing readmissions remain unaccounted for. Further improvements in readmission models will likely require accounting for psychosocial and behavioral factors not currently captured by EHRs. Journal of Hospital Medicine 2016;11:473-480. © 2016 Society of Hospital Medicine.

Initial Development of a Computer Algorithm to Identify Patients With Breast and Lung Cancer Having Poor Prognosis in a Safety Net Hospital.

BACKGROUND: Physicians often have difficulty with prognostication and identification of patients who are in need of counseling about options for care at the end of life. Consequently, the objective of this study was to describe the initial stages in development of a computerized algorithm that will identify breast and lung cancer patients most in need of counseling about care options, including advance care planning, palliative care, and hospice. METHODS: Clinical and non-clinical data were extracted from the electronic medical record of breast and lung cancer patients admitted to a large, urban hospital for the year 2010. These data were used to create an electronic (e-EOL) algorithm designed to identify advanced cancer patients who could benefit from in-depth discussion about end-of-life care options. RESULTS: There were 369 eligible breast (42%) and lung (58%) cancer patients identified by ICD-9 code. The e-EOL algorithm identified 53 (14%) patients that met assigned criteria (presence of metastatic disease and albumin < 2.5 g/dl). The sensitivity, specificity, and positive predictive value of the first generation algorithm were 21%, 96%, and 91% when compared to physician expert chart review. Survival analysis showed that 6-month survival for algorithm positive cases was 46% versus 78% for algorithm negative cases, and 1-year survival was 32% versus 72%, respectively. CONCLUSIONS: Initial testing of the e-EOL algorithm appears to be promising. Other markers of advanced illness will added to the algorithm to improve its test operating characteristics so it may be used to identify patients with poor prognosis in real time.

Electronic medical record-based multicondition models to predict the risk of 30 day readmission or death among adult medicine patients: validation and comparison to existing models.

BACKGROUND: There is increasing interest in using prediction models to identify patients at risk of readmission or death after hospital discharge, but existing models have significant limitations. Electronic medical record (EMR) based models that can be used to predict risk on multiple disease conditions among a wide range of patient demographics early in the hospitalization are needed. The objective of this study was to evaluate the degree to which EMR-based risk models for 30-day readmission or mortality accurately identify high risk patients and to compare these models with published claims-based models. METHODS: Data were analyzed from all consecutive adult patients admitted to internal medicine services at 7 large hospitals belonging to 3 health systems in Dallas/Fort Worth between November 2009 and October 2010 and split randomly into derivation and validation cohorts. Performance of the model was evaluated against the Canadian LACE mortality or readmission model and the Centers for Medicare and Medicaid Services (CMS) Hospital Wide Readmission model. RESULTS: Among the 39,604 adults hospitalized for a broad range of medical reasons, 2.8% of patients died, 12.7% were readmitted, and 14.7% were readmitted or died within 30 days after discharge. The electronic multicondition models for the composite outcome of 30-day mortality or readmission had good discrimination using data available within 24 h of admission (C statistic 0.69; 95% CI, 0.68-0.70), or at discharge (0.71; 95% CI, 0.70-0.72), and were significantly better than the LACE model (0.65; 95% CI, 0.64-0.66; P =0.02) with significant NRI (0.16) and IDI (0.039, 95% CI, 0.035-0.044). The electronic multicondition model for 30-day readmission alone had good discrimination using data available within 24 h of admission (C statistic 0.66; 95% CI, 0.65-0.67) or at discharge (0.68; 95% CI, 0.67-0.69), and performed significantly better than the CMS model (0.61; 95% CI, 0.59-0.62; P < 0.01) with significant NRI (0.20) and IDI (0.037, 95% CI, 0.033-0.041). CONCLUSIONS: A new electronic multicondition model based on information derived from the EMR predicted mortality and readmission at 30 days, and was superior to previously published claims-based models.

Envisioning a social-health information exchange as a platform to support a patient-centered medical neighborhood: a feasibility study.

BACKGROUND: Social determinants directly contribute to poorer health, and coordination between healthcare and community-based resources is pivotal to addressing these needs. However, our healthcare system remains poorly equipped to address social determinants of health. The potential of health information technology to bridge this gap across the delivery of healthcare and social services remains unrealized. OBJECTIVE, DESIGN, AND PARTICIPANTS: We conducted in-depth, in-person interviews with 50 healthcare and social service providers to determine the feasibility of a social-health information exchange (S-HIE) in an urban safety-net setting in Dallas County, Texas. After completion of interviews, we conducted a town hall meeting to identify desired functionalities for a S-HIE. APPROACH: We conducted thematic analysis of interview responses using the constant comparative method to explore perceptions about current communication and coordination across sectors, and barriers and enablers to S-HIE implementation. We sought participant confirmation of findings and conducted a forced-rank vote during the town hall to prioritize potential S-HIE functionalities. KEY RESULTS: We found that healthcare and social service providers perceived a need for improved information sharing, communication, and care coordination across sectors and were enthusiastic about the potential of a S-HIE, but shared many technical, legal, and ethical concerns around cross-sector information sharing. Desired technical S-HIE functionalities encompassed fairly simple transactional operations such as the ability to view basic demographic information, visit and referral data, and medical history from both healthcare and social service settings. CONCLUSIONS: A S-HIE is an innovative and feasible approach to enabling better linkages between healthcare and social service providers. However, to develop S-HIEs in communities across the country, policy interventions are needed to standardize regulatory requirements, to foster increased IT capability and uptake among social service agencies, and to align healthcare and social service priorities to enable dissemination and broader adoption of this and similar IT initiatives.

The legal and ethical concerns that arise from using complex predictive analytics in health care.

Predictive analytics, or the use of electronic algorithms to forecast future events in real time, makes it possible to harness the power of big data to improve the health of patients and lower the cost of health care. However, this opportunity raises policy, ethical, and legal challenges. In this article we analyze the major challenges to implementing predictive analytics in health care settings and make broad recommendations for overcoming challenges raised in the four phases of the life cycle of a predictive analytics model: acquiring data to build the model, building and validating it, testing it in real-world settings, and disseminating and using it more broadly. For instance, we recommend that model developers implement governance structures that include patients and other stakeholders starting in the earliest phases of development. In addition, developers should be allowed to use already collected patient data without explicit consent, provided that they comply with federal regulations regarding research on human subjects and the privacy of health information.

Implementing electronic health care predictive analytics: considerations and challenges.

The use of predictive modeling for real-time clinical decision making is increasingly recognized as a way to achieve the Triple Aim of improving outcomes, enhancing patients' experiences, and reducing health care costs. The development and validation of predictive models for clinical practice is only the initial step in the journey toward mainstream implementation of real-time point-of-care predictions. Integrating electronic health care predictive analytics (e-HPA) into the clinical work flow, testing e-HPA in a patient population, and subsequently disseminating e-HPA across US health care systems on a broad scale require thoughtful planning. Input is needed from policy makers, health care executives, researchers, and practitioners as the field evolves. This article describes some of the considerations and challenges of implementing e-HPA, including the need to ensure patients' privacy, establish a health system monitoring team to oversee implementation, incorporate predictive analytics into medical education, and make sure that electronic systems do not replace or crowd out decision making by physicians and patients.

Allocating scarce resources in real-time to reduce heart failure readmissions: a prospective, controlled study.

OBJECTIVE: To test a multidisciplinary approach to reduce heart failure (HF) readmissions that tailors the intensity of care transition intervention to the risk of the patient using a suite of electronic medical record (EMR)-enabled programmes. METHODS: A prospective controlled before and after study of adult inpatients admitted with HF and two concurrent control conditions (acute myocardial infarction (AMI) and pneumonia (PNA)) was performed between 1 December 2008 and 1 December 2010 at a large urban public teaching hospital. An EMR-based software platform stratified all patients admitted with HF on a daily basis by their 30-day readmission risk using a published electronic predictive model. Patients at highest risk received an intensive set of evidence-based interventions designed to reduce readmission using existing resources. The main outcome measure was readmission for any cause and to any hospital within 30 days of discharge. RESULTS: There were 834 HF admissions in the pre-intervention period and 913 in the post-intervention period. The unadjusted readmission rate declined from 26.2% in the pre-intervention period to 21.2% in the post-intervention period (p=0.01), a decline that persisted in adjusted analyses (adjusted OR (AOR)=0.73; 95% CI 0.58 to 0.93, p=0.01). In contrast, there was no significant change in the unadjusted and adjusted readmission rates for PNA and AMI over the same period. There were 45 fewer readmissions with 913 patients enrolled and 228 patients receiving intervention, resulting in a number needed to treat (NNT) ratio of 20. CONCLUSIONS: An EMR-enabled strategy that targeted scarce care transition resources to high-risk HF patients significantly reduced the risk-adjusted odds of readmission.

Identifying patients with diabetes and the earliest date of diagnosis in real time: an electronic health record case-finding algorithm.

BACKGROUND: Effective population management of patients with diabetes requires timely recognition. Current case-finding algorithms can accurately detect patients with diabetes, but lack real-time identification. We sought to develop and validate an automated, real-time diabetes case-finding algorithm to identify patients with diabetes at the earliest possible date. METHODS: The source population included 160,872 unique patients from a large public hospital system between January 2009 and April 2011. A diabetes case-finding algorithm was iteratively derived using chart review and subsequently validated (n = 343) in a stratified random sample of patients, using data extracted from the electronic health records (EHR). A point-based algorithm using encounter diagnoses, clinical history, pharmacy data, and laboratory results was used to identify diabetes cases. The date when accumulated points reached a specified threshold equated to the diagnosis date. Physician chart review served as the gold standard. RESULTS: The electronic model had a sensitivity of 97%, specificity of 90%, positive predictive value of 90%, and negative predictive value of 96% for the identification of patients with diabetes. The kappa score for agreement between the model and physician for the diagnosis date allowing for a 3-month delay was 0.97, where 78.4% of cases had exact agreement on the precise date. CONCLUSIONS: A diabetes case-finding algorithm using data exclusively extracted from a comprehensive EHR can accurately identify patients with diabetes at the earliest possible date within a healthcare system. The real-time capability may enable proactive disease management.

An automated model using electronic medical record data identifies patients with cirrhosis at high risk for readmission.

BACKGROUND & AIMS: Patients with cirrhosis have 1-month rates of readmission as high as 35%. Early identification of high-risk patients could permit interventions to reduce readmission. The aim of our study was to construct an automated 30-day readmission risk model for cirrhotic patients using electronic medical record (EMR) data available early during hospitalization. METHODS: We identified patients with cirrhosis admitted to a large safety-net hospital from January 2008 through December 2009. A multiple logistic regression model for 30-day rehospitalization was developed using medical and socioeconomic factors available within 48 hours of admission and tested on a validation cohort. Discrimination was assessed using receiver operator characteristic curve analysis. RESULTS: We identified 836 cirrhotic patients with 1291 unique admission encounters. Rehospitalization occurred within 30 days for 27% of patients. Significant predictors of 30-day readmission included the number of address changes in the prior year (odds ratio [OR], 1.13; 95% confidence interval [CI], 1.05-1.21), number of admissions in the prior year (OR, 1.14; 95% CI, 1.05-1.24), Medicaid insurance (OR, 1.53; 95% CI, 1.10-2.13), thrombocytopenia (OR, 0.50; 95% CI, 0.35-0.72), low level of alanine aminotransferase (OR, 2.56; 95% CI, 1.09-6.00), anemia (OR, 1.63; 95% CI, 1.17-2.27), hyponatremia (OR, 1.78; 95% CI, 1.14-2.80), and Model for End-stage Liver Disease score (OR, 1.04; 95% CI, 1.01-1.06). The risk model predicted 30-day readmission, with c-statistics of 0.68 (95% CI, 0.64-0.72) and 0.66 (95% CI, 0.59-0.73) in the derivation and validation cohorts, respectively. CONCLUSIONS: Clinical and social factors available early during admission and extractable from an EMR predicted 30-day readmission in cirrhotic patients with moderate accuracy. Decision support tools that use EMR-automated data are useful for risk stratification of patients with cirrhosis early during hospitalization.

Predicting out of intensive care unit cardiopulmonary arrest or death using electronic medical record data.

BACKGROUND: Accurate, timely and automated identification of patients at high risk for severe clinical deterioration using readily available clinical information in the electronic medical record (EMR) could inform health systems to target scarce resources and save lives. METHODS: We identified 7,466 patients admitted to a large, public, urban academic hospital between May 2009 and March 2010. An automated clinical prediction model for out of intensive care unit (ICU) cardiopulmonary arrest and unexpected death was created in the derivation sample (50% randomly selected from total cohort) using multivariable logistic regression. The automated model was then validated in the remaining 50% from the total cohort (validation sample). The primary outcome was a composite of resuscitation events, and death (RED). RED included cardiopulmonary arrest, acute respiratory compromise and unexpected death. Predictors were measured using data from the previous 24 hours. Candidate variables included vital signs, laboratory data, physician orders, medications, floor assignment, and the Modified Early Warning Score (MEWS), among other treatment variables. RESULTS: RED rates were 1.2% of patient-days for the total cohort. Fourteen variables were independent predictors of RED and included age, oxygenation, diastolic blood pressure, arterial blood gas and laboratory values, emergent orders, and assignment to a high risk floor. The automated model had excellent discrimination (c-statistic=0.85) and calibration and was more sensitive (51.6% and 42.2%) and specific (94.3% and 91.3%) than the MEWS alone. The automated model predicted RED 15.9 hours before they occurred and earlier than Rapid Response Team (RRT) activation (5.7 hours prior to an event, p=0.003) CONCLUSION: An automated model harnessing EMR data offers great potential for identifying RED and was superior to both a prior risk model and the human judgment-driven RRT.

Use of administrative claims data for identifying patients with cirrhosis.

BACKGROUND: Administrative data are used in clinical research, but the validity of ICD-9 codes to identify cirrhotic patients has not been well established. GOALS: To determine the diagnostic accuracy of ICD-9 codes for cirrhosis in clinical practice. STUDY: We conducted a retrospective cohort study of patients from a safety-net hospital between 2008 and 2011. Patients were initially identified using ICD-9 codes for cirrhosis or a resultant complication. The gold-standard for diagnosis of cirrhosis was histology and/or imaging based on medical record review. Sensitivity, specificity, positive predictive values, and negative predictive values for each ICD-9 code were calculated. Diagnostic accuracy was assessed by the c-statistic using receiver operator characteristic curve analysis. RESULTS: We identified 2893 patients with an ICD-9 code for cirrhosis, of whom 50.2% had 1 ICD-9 code, 20.3% had 2 different codes, and 29.5% had 3 or more codes. Cirrhosis was confirmed in 44.0% of patients with 1 ICD-9 code, 82.6% with 2 codes, and 95.7% of those with at least 3 codes. Ascites had a significantly lower positive predictive values for cirrhosis than other ICD-9 codes (P<0.001). The optimal combination of ICD-9 codes to identify cirrhotic patients included all codes except that of ascites, with a c-statistic of 0.71 in our derivation cohort. The sensitivity of this combination was confirmed to be 98% in a validation cohort of 285 patients with known cirrhosis. CONCLUSIONS: Administrative data can identify patients with cirrhosis with high accuracy, although ascites has a significantly lower positive predictive value than other ICD-9 codes.

Applying data analytics and information exchange to improve care for patients.

Parkland Hospital's Ruben Amarasingham built a model to predict patients at high risk for readmission and now leads efforts to extend the benefits of health information to the nation's most vulnerable.

An electronic medical record-based model to predict 30-day risk of readmission and death among HIV-infected inpatients.

BACKGROUND: Readmission after hospitalization is costly, time-consuming, and remains common among HIV-infected individuals. We sought to use data from the Electronic Medical Record (EMR) to create a clinical, robust, multivariable model for predicting readmission risk in hospitalized HIV-infected patients. METHODS: We extracted clinical and nonclinical data from the EMR of HIV-infected patients admitted to a large urban hospital between March 2006 and November 2008. These data were used to build automated predictive models for 30-day risk of readmission and death. RESULTS: We identified 2476 index admissions among HIV-infected inpatients who were 73% males, 57% African American, with a mean age of 43 years. One-quarter were readmitted, and 3% died within 30 days of discharge. Those with a primary diagnosis during the index admission of HIV/AIDS accounted for the largest proportion of readmissions (41%), followed by those initially admitted for other infections (10%) or for oncologic (6%), pulmonary (5%), gastrointestinal (4%), and renal (3%) causes. Factors associated with readmission risk include: AIDS defining illness, CD4 ≤ 92, laboratory abnormalities, insurance status, homelessness, distance from the hospital, and prior emergency department visits and hospitalizations (c = 0.72; 95% confidence interval: 0.70 to 0.75). The multivariable predictors of death were CD4 < 132, abnormal liver function tests, creatinine >1.66, and hematocrit <30.8 (c = 0.79; 95% confidence interval: 0.74 to 0.84) for death. CONCLUSIONS: Readmission rates among HIV-infected patients were high. An automated model composed of factors accessible from the EMR in the first 48 hours of admission performed well in predicting the 30-day risk of readmission among HIV patients. Such a model could be used in real-time to identify HIV patients at highest risk so readmission prevention resources could be targeted most efficiently.

An automated model to identify heart failure patients at risk for 30-day readmission or death using electronic medical record data.

BACKGROUND: A real-time electronic predictive model that identifies hospitalized heart failure (HF) patients at high risk for readmission or death may be valuable to clinicians and hospitals who care for these patients. METHODS: An automated predictive model for 30-day readmission and death was derived and validated from clinical and nonclinical risk factors present on admission in 1372 HF hospitalizations to a major urban hospital between January 2007 and August 2008. Data were extracted from an electronic medical record. The performance of the electronic model was compared with mortality and readmission models developed by the Center for Medicaid and Medicare Services (CMS models) and a HF mortality model derived from the Acute Decompensated Heart Failure Registry (ADHERE model). RESULTS: The 30-day mortality and readmission rates were 3.1% and 24.1% respectively. The electronic model demonstrated good discrimination for 30 day mortality (C statistic 0.86) and readmission (C statistic 0.72) and performed as well, or better than, the ADHERE model and CMS models for both outcomes (C statistic ranges: 0.72-0.73 and 0.56-0.66 for mortality and readmissions respectively; P < 0.05 in all comparisons). Markers of social instability and lower socioeconomic status improved readmission prediction in the electronic model (C statistic 0.72 vs. 0.61, P < 0.05). CONCLUSIONS: Clinical and social factors available within hours of hospital presentation and extractable from an EMR predicted mortality and readmission at 30 days. Incorporating complex social factors increased the model's accuracy, suggesting that such factors could enhance risk adjustment models designed to compare hospital readmission rates.

A rapid admission protocol to reduce emergency department boarding times.

INTRODUCTION: Prolonged emergency department boarding times (EDBT) are associated with adverse clinical outcomes and longer hospital stays. A rapid admission protocol was designed at our institution to reduce both EDBT and time to admission orders (EDTAO) for patients admitted to the internal medicine service. METHODS: The existing admission process was examined by a team of clinical and administrative leaders who focused on developing a change management architecture, narrowing clinical roles, mandating direct communication, establishing clear boundaries for patient responsibility and instituting carefully constructed holding orders. The number of steps in the admission process was reduced from 50 to 10. We collected EDBT and EDTAO for all patients admitted to the internal medicine service before and after intervention using a simple interrupted time-series design. RESULTS: The study involved a total of 9604 admissions to one of three inpatient destinations (general medicine ward, telemetry or intensive care unit). The overall EDBT decreased from 360 min in the preintervention period to 270 min in phase 4 (p<0.001). The overall time to admission orders decreased from 210 min in the preintervention period to 75 min in phase 4 (p<0.001) overall. However, no improvements were noted in EDBT for telemetry or ICU patients. CONCLUSIONS: Institution of a rapid admission protocol successfully reduced overall EDBT at our institution, although few gains were noted for patients with a telemetry or ICU destination. In total, the intervention saved 27 884 h, or 1161 emergency department patient-days, over the course of a single year.

Clinical information technologies and inpatient outcomes: a multiple hospital study.

BACKGROUND: Despite speculation that clinical information technologies will improve clinical and financial outcomes, few studies have examined this relationship in a large number of hospitals. METHODS: We conducted a cross-sectional study of urban hospitals in Texas using the Clinical Information Technology Assessment Tool, which measures a hospital's level of automation based on physician interactions with the information system. After adjustment for potential confounders, we examined whether greater automation of hospital information was associated with reduced rates of inpatient mortality, complications, costs, and length of stay for 167 233 patients older than 50 years admitted to responding hospitals between December 1, 2005, and May 30, 2006. RESULTS: We received a sufficient number of responses from 41 of 72 hospitals (58%). For all medical conditions studied, a 10-point increase in the automation of notes and records was associated with a 15% decrease in the adjusted odds of fatal hospitalizations (0.85; 95% confidence interval, 0.74-0.97). Higher scores in order entry were associated with 9% and 55% decreases in the adjusted odds of death for myocardial infarction and coronary artery bypass graft procedures, respectively. For all causes of hospitalization, higher scores in decision support were associated with a 16% decrease in the adjusted odds of complications (0.84; 95% confidence interval, 0.79-0.90). Higher scores on test results, order entry, and decision support were associated with lower costs for all hospital admissions (-$110, -$132, and -$538, respectively; P < .05). CONCLUSION: Hospitals with automated notes and records, order entry, and clinical decision support had fewer complications, lower mortality rates, and lower costs.

Hospital characteristics associated with highly automated and usable clinical information systems in Texas, United States.

BACKGROUND: A hospital's clinical information system may require a specific environment in which to flourish. This environment is not yet well defined. We examined whether specific hospital characteristics are associated with highly automated and usable clinical information systems. METHODS: This was a cross-sectional survey of 125 urban hospitals in Texas, United States using the Clinical Information Technology Assessment Tool (CITAT), which measures a hospital's level of automation based on physician interactions with the information system. Physician responses were used to calculate a series of CITAT scores: automation and usability scores, four automation sub-domain scores, and an overall clinical information technology (CIT) score. A multivariable regression analysis was used to examine the relation between hospital characteristics and CITAT scores. RESULTS: We received a sufficient number of physician responses at 69 hospitals (55% response rate). Teaching hospitals, hospitals with higher IT operating expenses (>$1 million annually), IT capital expenses (>$75,000 annually) and hospitals with larger IT staff (> or = 10 full-time staff) had higher automation scores than hospitals that did not meet these criteria (p < 0.05 in all cases). These findings held after adjustment for bed size, total margin, and ownership (p < 0.05 in all cases). There were few significant associations between the hospital characteristics tested in this study and usability scores. CONCLUSION: Academic affiliation and larger IT operating, capital, and staff budgets are associated with more highly automated clinical information systems.