Patterns of multimorbidity associated with 30-day readmission: a multinational study.

BACKGROUND: Multimorbidity is associated with higher healthcare utilization; however, data exploring its association with readmission are scarce. We aimed to investigate which most important patterns of multimorbidity are associated with 30-day readmission. METHODS: We used a multinational retrospective cohort of 126,828 medical inpatients with multimorbidity defined as ≥2 chronic diseases. The primary and secondary outcomes were 30-day potentially avoidable readmission (PAR) and 30-day all-cause readmission (ACR), respectively. Only chronic diseases were included in the analyses. We presented the OR for readmission according to the number of diseases or body systems involved, and the combinations of diseases categories with the highest OR for readmission. RESULTS: Multimorbidity severity, assessed as number of chronic diseases or body systems involved, was strongly associated with PAR, and to a lesser extend with ACR. The strength of association steadily and linearly increased with each additional disease or body system involved. Patients with four body systems involved or nine diseases already had a more than doubled odds for PAR (OR 2.35, 95%CI 2.15-2.57, and OR 2.25, 95%CI 2.05-2.48, respectively). The combinations of diseases categories that were most strongly associated with PAR and ACR were chronic kidney disease with liver disease or chronic ulcer of skin, and hematological malignancy with esophageal disorders or mood disorders, respectively. CONCLUSIONS: Readmission was associated with the number of chronic diseases or body systems involved and with specific combinations of diseases categories. The number of body systems involved may be a particularly interesting measure of the risk for readmission in multimorbid patients.

Preventability of Early Versus Late Hospital Readmissions in a National Cohort of General Medicine Patients.

Background: Many experts believe that hospitals with more frequent readmissions provide lower-quality care, but little is known about how the preventability of readmissions might change over the postdischarge time frame. Objective: To determine whether readmissions within 7 days of discharge differ from those between 8 and 30 days after discharge with respect to preventability. Design: Prospective cohort study. Setting: 10 academic medical centers in the United States. Patients: 822 adults readmitted to a general medicine service. Measurements: For each readmission, 2 site-specific physician adjudicators used a structured survey instrument to determine whether it was preventable and measured other characteristics. Results: Overall, 36.2% of early readmissions versus 23.0% of late readmissions were preventable (median risk difference, 13.0 percentage points [interquartile range, 5.5 to 26.4 percentage points]). Hospitals were identified as better locations for preventing early readmissions (47.2% vs. 25.5%; median risk difference, 22.8 percentage points [interquartile range, 17.9 to 31.8 percentage points]), whereas outpatient clinics (15.2% vs. 6.6%; median risk difference, 10.0 percentage points [interquartile range, 4.6 to 12.2 percentage points]) and home (19.4% vs. 14.0%; median risk difference, 5.6 percentage points [interquartile range, -6.1 to 17.1 percentage points]) were better for preventing late readmissions. Limitation: Physician adjudicators were not blinded to readmission timing, community hospitals were not included in the study, and readmissions to nonstudy hospitals were not included in the results. Conclusion: Early readmissions were more likely to be preventable and amenable to hospital-based interventions. Late readmissions were less likely to be preventable and were more amenable to ambulatory and home-based interventions. Primary Funding Source: Association of American Medical Colleges.

The HOSPITAL Score Predicts Potentially Preventable 30-Day Readmissions in Conditions Targeted by the Hospital Readmissions Reduction Program.

BACKGROUND/OBJECTIVES: New tools to accurately identify potentially preventable 30-day readmissions are needed. The HOSPITAL score has been internationally validated for medical inpatients, but its performance in select conditions targeted by the Hospital Readmission Reduction Program (HRRP) is unknown. DESIGN: Retrospective cohort study. SETTING: Six geographically diverse medical centers. PARTICIPANTS/EXPOSURES: All consecutive adult medical patients discharged alive in 2011 with 1 of the 4 medical conditions targeted by the HRRP (acute myocardial infarction, chronic obstructive pulmonary disease, pneumonia, and heart failure) were included. Potentially preventable 30-day readmissions were identified using the SQLape algorithm. The HOSPITAL score was calculated for all patients. MEASUREMENTS: A multivariable logistic regression model accounting for hospital effects was used to evaluate the accuracy (Brier score), discrimination (c-statistic), and calibration (Pearson goodness-of-fit) of the HOSPITAL score for each 4 medical conditions. RESULTS: Among the 9181 patients included, the overall 30-day potentially preventable readmission rate was 13.6%. Across all 4 diagnoses, the HOSPITAL score had very good accuracy (Brier score of 0.11), good discrimination (c-statistic of 0.68), and excellent calibration (Hosmer-Lemeshow goodness-of-fit test, P=0.77). Within each diagnosis, performance was similar. In sensitivity analyses, performance was similar for all readmissions (not just potentially preventable) and when restricted to patients age 65 and above. CONCLUSIONS: The HOSPITAL score identifies a high-risk cohort for potentially preventable readmissions in a variety of practice settings, including conditions targeted by the HRRP. It may be a valuable tool when included in interventions to reduce readmissions within or across these conditions.

Effect of a Real-Time Electronic Dashboard on a Rapid Response System.

A rapid response system (RRS) may have limited effectiveness when inpatient providers fail to recognize signs of early patient decompensation. We evaluated the impact of an electronic medical record (EMR)-based alerting dashboard on outcomes associated with RRS activation. We used a repeated treatment study in which the dashboard display was successively turned on and off each week for ten 2-week cycles over a 20-week period on the inpatient acute care wards of an academic medical center. The Rapid Response Team (RRT) dashboard displayed all hospital patients in a single view ranked by severity score, updated in real time. The dashboard could be seen within the EMR by any provider, including RRT members. The primary outcomes were the incidence rate ratio (IRR) of all RRT activations, unexpected ICU transfers, cardiopulmonary arrests and deaths on general medical-surgical wards (wards). We conducted an exploratory analysis of first RRT activations. There were 6736 eligible admissions during the 20-week study period. There was no change in overall RRT activations (IRR = 1.14, p = 0.07), but a significant increase in first RRT activations (IRR = 1.20, p = 0.04). There were no significant differences in unexpected ICU transfers (IRR = 1.15, p = 0.25), cardiopulmonary arrests on general wards (IRR = 1.46, p = 0.43), or deaths on general wards (IRR = 0.96, p = 0.89). The introduction of the RRT dashboard was associated with increased initial RRT activations but not overall activations, unexpected ICU transfers, cardiopulmonary arrests, or death. The RRT dashboard is a novel tool to help providers recognize patient decompensation and may improve initial RRT notification.

Association of patterns of multimorbidity with length of stay: A multinational observational study.

The aim of this study was to identify the combinations of chronic comorbidities associated with length of stay (LOS) among multimorbid medical inpatients.Multinational retrospective cohort of 126,828 medical inpatients with multimorbidity, defined as ≥2 chronic diseases (data collection: 2010-2011). We categorized the chronic diseases into comorbidities using the Clinical Classification Software. We described the 20 combinations of comorbidities with the strongest association with prolonged LOS, defined as longer than or equal to country-specific LOS, and reported the difference in median LOS for those combinations. We also assessed the association between the number of diseases or body systems involved and prolonged LOS.The strongest association with prolonged LOS (odds ratio [OR] 7.25, 95% confidence interval [CI] 6.64-7.91, P < 0.001) and the highest difference in median LOS (13 days, 95% CI 12.8-13.2, P < 0.001) were found for the combination of diseases of white blood cells and hematological malignancy. Other comorbidities found in the 20 top combinations had ORs between 2.37 and 3.65 (all with P < 0.001) and a difference in median LOS of 2 to 5 days (all with P < 0.001), and included mostly neurological disorders and chronic ulcer of skin. Prolonged LOS was associated with the number of chronic diseases and particularly with the number of body systems involved (≥7 body systems: OR 21.50, 95% CI 19.94-23.18, P < 0.001).LOS was strongly associated with specific combinations of comorbidities and particularly with the number of body systems involved. Describing patterns of multimorbidity associated with LOS may help hospitals anticipate resource utilization and judiciously allocate services to shorten LOS.

Patterns of multimorbidity in medical inpatients: a multinational retrospective cohort study.

Multimorbidity is frequent and represents a significant burden for patients and healthcare systems. However, there are limited data on the most common combinations of comorbidities in multimorbid patients. We aimed to describe and quantify the most common combinations of comorbidities in multimorbid medical inpatients. We used a large retrospective cohort of adults discharged from the medical department of 11 hospitals across 3 countries (USA, Switzerland, and Israel) between 2010 and 2011. Diseases were classified into acute versus chronic. Chronic diseases were grouped into clinically meaningful categories of comorbidities. We identified the most prevalent combinations of comorbidities and compared the observed and expected prevalence of the combinations. We assessed the distribution of acute and chronic diseases and the median number of body systems in relationship to the total number of diseases. Eighty-six percent (n = 126,828/147,806) of the patients were multimorbid (≥ 2 chronic diseases), with a median of five chronic diseases; 13% of the patients had ≥ 10 chronic diseases. Among the most frequent combinations of comorbidities, the most prevalent comorbidity was chronic heart disease. Other high prevalent comorbidities included mood disorders, arthropathy and arthritis, and esophageal disorders. The ratio of chronic versus acute diseases was approximately 2:1. Multimorbidity affected almost 90% of patients, with a median of five chronic diseases. Over 10% had ≥ 10 chronic diseases. This identification and quantification of frequent combinations of comorbidities among multimorbid medical inpatients may increase awareness of what should be taken into account when treating such patients, a growth in the need for special care considerations.

Best Definitions of Multimorbidity to Identify Patients With High Health Care Resource Utilization.

OBJECTIVE: To compare different definitions of multimorbidity to identify patients with higher health care resource utilization. PATIENTS AND METHODS: We used a multinational retrospective cohort including 147,806 medical inpatients discharged from 11 hospitals in 3 countries (United States, Switzerland, and Israel) between January 1, 2010, and December 31, 2011. We compared the area under the receiver operating characteristic curve (AUC) of 8 definitions of multimorbidity, based on International Classification of Diseases codes defining health conditions, the Deyo-Charlson Comorbidity Index, the Elixhauser-van Walraven Comorbidity Index, body systems, or Clinical Classification Software categories to predict 30-day hospital readmission and/or prolonged length of stay (longer than or equal to the country-specific upper quartile). We used a lower (yielding sensitivity ≥90%) and an upper (yielding specificity ≥60%) cutoff to create risk categories. RESULTS: Definitions had poor to fair discriminatory power in the derivation (AUC, 0.61-0.65) and validation cohorts (AUC, 0.64-0.71). The definitions with the highest AUC were number of (1) health conditions with involvement of 2 or more body systems, (2) body systems, (3) Clinical Classification Software categories, and (4) health conditions. At the upper cutoff, sensitivity and specificity were 65% to 79% and 50% to 53%, respectively, in the validation cohort; of the 147,806 patients, 5% to 12% (7474 to 18,008) were classified at low risk, 38% to 55% (54,484 to 81,540) at intermediate risk, and 32% to 50% (47,331 to 72,435) at high risk. CONCLUSION: Of the 8 definitions of multimorbidity, 4 had comparable discriminatory power to identify patients with higher health care resource utilization. Of these 4, the number of health conditions may represent the easiest definition to apply in clinical routine. The cutoff chosen, favoring sensitivity or specificity, should be determined depending on the aim of the definition.

Multicenter validation of a machine-learning algorithm for 48-h all-cause mortality prediction.

In order to evaluate mortality predictions based on boosted trees, this retrospective study uses electronic medical record data from three academic health centers for inpatients 18 years or older with at least one observation of each vital sign. Predictions were made 12, 24, and 48 hours before death. Models fit to training data from each institution were evaluated using hold-out test data from the same institution, and from the other institutions. Gradient-boosted trees (GBT) were compared to regularized logistic regression (LR) predictions, support vector machine (SVM) predictions, quick Sepsis-Related Organ Failure Assessment (qSOFA), and Modified Early Warning Score (MEWS) using area under the receiver operating characteristic curve (AUROC). For training and testing GBT on data from the same institution, the average AUROCs were 0.96, 0.95, and 0.94 across institutional test sets for 12-, 24-, and 48-hour predictions, respectively. When trained and tested on data from different hospitals, GBT AUROCs achieved up to 0.98, 0.96, and 0.96, for 12-, 24-, and 48-hour predictions, respectively. Average AUROC for 48-hour predictions for LR, SVM, MEWS, and qSOFA were 0.85, 0.79, 0.86 and 0.82, respectively. GBT predictions may help identify patients who would benefit from increased clinical care.

Comparative prognostic accuracy of sepsis scores for hospital mortality in adults with suspected infection in non-ICU and ICU at an academic public hospital.

BACKGROUND: Sepsis is a global healthcare challenge and reliable tools are needed to identify patients and stratify their risk. Here we compare the prognostic accuracy of the sepsis-related organ failure assessment (SOFA), quick SOFA (qSOFA), systemic inflammatory response syndrome (SIRS), and national early warning system (NEWS) scores for hospital mortality and other outcomes amongst patients with suspected infection at an academic public hospital. MEASUREMENTS AND MAIN RESULTS: 10,981 adult patients with suspected infection hospitalized at a U.S. academic public hospital between 2011-2017 were retrospectively identified. Primary exposures were the maximum SIRS, qSOFA, SOFA, and NEWS scores upon inclusion. Comparative prognostic accuracy for the primary outcome of hospital mortality was assessed using the area under the receiver operating characteristic curve (AUROC). Secondary outcomes included mortality in ICU versus non-ICU settings, ICU transfer, ICU length of stay (LOS) >3 days, and hospital LOS >7 days. Adjusted analyses were performed using a model of baseline risk for hospital mortality. 774 patients (7.1%) died in hospital. Discrimination for hospital mortality was highest for SOFA (AUROC 0.90 [95% CI, 0.89-0.91]), followed by NEWS (AUROC 0.85 [95% CI, 0.84-0.86]), qSOFA (AUROC 0.84 [95% CI, 0.83-0.85]), and SIRS (AUROC 0.79 [95% CI, 0.78-0.81]; p<0.001 for all comparisons). NEWS (AUROC 0.94 [95% CI, 0.93-0.95]) outperformed other scores in predicting ICU transfer (qSOFA AUROC 0.89 [95% CI, 0.87-0.91]; SOFA AUROC, 0.84 [95% CI, 0.82-0.87]; SIRS AUROC 0.81 [95% CI, 0.79-0.83]; p<0.001 for all comparisons). NEWS (AUROC 0.86 [95% CI, 0.85-0.86]) was also superior to other scores in predicting ICU LOS >3 days (SOFA AUROC 0.84 [95% CI, 0.83-0.85; qSOFA AUROC, 0.83 [95% CI, 0.83-0.84]; SIRS AUROC, 0.75 [95% CI, 0.74-0.76]; p<0.002 for all comparisons). CONCLUSIONS: Multivariate prediction scores, such as SOFA and NEWS, had greater prognostic accuracy than qSOFA or SIRS for hospital mortality, ICU transfer, and ICU length of stay. Complex sepsis scores may offer enhanced prognostic performance as compared to simple sepsis scores in inpatient hospital settings where more complex scores can be readily calculated.

Correction: Comparative prognostic accuracy of sepsis scores for hospital mortality in adults with suspected infection in non-ICU and ICU at an academic public hospital.

[This corrects the article DOI: 10.1371/journal.pone.0222563.].

Multicentre validation of a sepsis prediction algorithm using only vital sign data in the emergency department, general ward and ICU.

OBJECTIVES: We validate a machine learning-based sepsis-prediction algorithm (InSight) for the detection and prediction of three sepsis-related gold standards, using only six vital signs. We evaluate robustness to missing data, customisation to site-specific data using transfer learning and generalisability to new settings. DESIGN: A machine-learning algorithm with gradient tree boosting. Features for prediction were created from combinations of six vital sign measurements and their changes over time. SETTING: A mixed-ward retrospective dataset from the University of California, San Francisco (UCSF) Medical Center (San Francisco, California, USA) as the primary source, an intensive care unit dataset from the Beth Israel Deaconess Medical Center (Boston, Massachusetts, USA) as a transfer-learning source and four additional institutions' datasets to evaluate generalisability. PARTICIPANTS: 684 443 total encounters, with 90 353 encounters from June 2011 to March 2016 at UCSF. INTERVENTIONS: None. PRIMARY AND SECONDARY OUTCOME MEASURES: Area under the receiver operating characteristic (AUROC) curve for detection and prediction of sepsis, severe sepsis and septic shock. RESULTS: For detection of sepsis and severe sepsis, InSight achieves an AUROC curve of 0.92 (95% CI 0.90 to 0.93) and 0.87 (95% CI 0.86 to 0.88), respectively. Four hours before onset, InSight predicts septic shock with an AUROC of 0.96 (95% CI 0.94 to 0.98) and severe sepsis with an AUROC of 0.85 (95% CI 0.79 to 0.91). CONCLUSIONS: InSight outperforms existing sepsis scoring systems in identifying and predicting sepsis, severe sepsis and septic shock. This is the first sepsis screening system to exceed an AUROC of 0.90 using only vital sign inputs. InSight is robust to missing data, can be customised to novel hospital data using a small fraction of site data and retains strong discrimination across all institutions.

Selection and Implementation of an Electronic Health Record.

Electronic health records (EHRs) are now the standard of practice in most communities, because of transition to reimbursement that increasingly focuses on risk sharing and quality measurement, and government EHR incentive programs. The selection and implementation of an EHR is one of the most important decisions a practice faces. Organizing the search for an EHR that fits a practice, negotiating a contract, planning and successfully implementing an EHR are best accomplished with a well-informed, strong, multidisciplinary team using project management techniques. Focusing on the best match between your practice's needs and available commercial systems, and creating a strong relationship with your vendor will be key to leveraging the EHR to improve the experience of your patients and the quality of care they receive, and to the efficiency of your practice.

Using Transfer Learning for Improved Mortality Prediction in a Data-Scarce Hospital Setting.

Algorithm-based clinical decision support (CDS) systems associate patient-derived health data with outcomes of interest, such as in-hospital mortality. However, the quality of such associations often depends on the availability of site-specific training data. Without sufficient quantities of data, the underlying statistical apparatus cannot differentiate useful patterns from noise and, as a result, may underperform. This initial training data burden limits the widespread, out-of-the-box, use of machine learning-based risk scoring systems. In this study, we implement a statistical transfer learning technique, which uses a large "source" data set to drastically reduce the amount of data needed to perform well on a "target" site for which training data are scarce. We test this transfer technique with AutoTriage, a mortality prediction algorithm, on patient charts from the Beth Israel Deaconess Medical Center (the source) and a population of 48 249 adult inpatients from University of California San Francisco Medical Center (the target institution). We find that the amount of training data required to surpass 0.80 area under the receiver operating characteristic (AUROC) on the target set decreases from more than 4000 patients to fewer than 220. This performance is superior to the Modified Early Warning Score (AUROC: 0.76) and corresponds to a decrease in clinical data collection time from approximately 6 months to less than 10 days. Our results highlight the usefulness of transfer learning in the specialization of CDS systems to new hospital sites, without requiring expensive and time-consuming data collection efforts.

Simplification of the HOSPITAL score for predicting 30-day readmissions.

OBJECTIVE: The HOSPITAL score has been widely validated and accurately identifies high-risk patients who may mostly benefit from transition care interventions. Although this score is easy to use, it has the potential to be simplified without impacting its performance. We aimed to validate a simplified version of the HOSPITAL score for predicting patients likely to be readmitted. DESIGN AND SETTING: Retrospective study in 9 large hospitals across 4 countries, from January through December 2011. PARTICIPANTS: We included all consecutively discharged medical patients. We excluded patients who died before discharge or were transferred to another acute care facility. MEASUREMENTS: The primary outcome was any 30-day potentially avoidable readmission. We simplified the score as follows: (1) 'discharge from an oncology division' was replaced by 'cancer diagnosis or discharge from an oncology division'; (2) 'any procedure' was left out; (3) patients were categorised into two risk groups (unlikely and likely to be readmitted). The performance of the simplified HOSPITAL score was evaluated according to its overall accuracy, its discriminatory power and its calibration. RESULTS: Thirty-day potentially avoidable readmission rate was 9.7% (n=11 307/117 065 patients discharged). Median of the simplified HOSPITAL score was 3 points (IQR 2-5). Overall accuracy was very good with a Brier score of 0.08 and discriminatory power remained good with a C-statistic of 0.69 (95% CI 0.68 to 0.69). The calibration was excellent when comparing the expected with the observed risk in the two risk categories. CONCLUSIONS: The simplified HOSPITAL score has good performance for predicting 30-day readmission. Prognostic accuracy was similar to the original version, while its use is even easier. This simplified score may provide a good alternative to the original score depending on the setting.

International Validity of the HOSPITAL Score to Predict 30-Day Potentially Avoidable Hospital Readmissions.

IMPORTANCE: Identification of patients at a high risk of potentially avoidable readmission allows hospitals to efficiently direct additional care transitions services to the patients most likely to benefit. OBJECTIVE: To externally validate the HOSPITAL score in an international multicenter study to assess its generalizability. DESIGN, SETTING, AND PARTICIPANTS: International retrospective cohort study of 117 065 adult patients consecutively discharged alive from the medical department of 9 large hospitals across 4 different countries between January 2011 and December 2011. Patients transferred to another acute care facility were excluded. EXPOSURES: The HOSPITAL score includes the following predictors at discharge: hemoglobin, discharge from an oncology service, sodium level, procedure during the index admission, index type of admission (urgent), number of admissions during the last 12 months, and length of stay. MAIN OUTCOMES AND MEASURES: 30-day potentially avoidable readmission to the index hospital using the SQLape algorithm. RESULTS: Overall, 117 065 adults consecutively discharged alive from a medical department between January 2011 and December 2011 were studied. Of all medical discharges, 16 992 of 117 065 (14.5%) were followed by a 30-day readmission, and 11 307 (9.7%) were followed by a 30-day potentially avoidable readmission. The discriminatory power of the HOSPITAL score to predict potentially avoidable readmission was good, with a C statistic of 0.72 (95% CI, 0.72-0.72). As in the derivation study, patients were classified into 3 risk categories: low (n = 73 031 [62.4%]), intermediate (n = 27 612 [23.6%]), and high risk (n = 16 422 [14.0%]). The estimated proportions of potentially avoidable readmission for each risk category matched the observed proportion, resulting in an excellent calibration (Pearson χ2 test P = .89). CONCLUSIONS AND RELEVANCE: The HOSPITAL score identified patients at high risk of 30-day potentially avoidable readmission with moderately high discrimination and excellent calibration when applied to a large international multicenter cohort of medical patients. This score has the potential to easily identify patients in need of more intensive transitional care interventions to prevent avoidable hospital readmissions.

Preventability and Causes of Readmissions in a National Cohort of General Medicine Patients.

IMPORTANCE: Readmission penalties have catalyzed efforts to improve care transitions, but few programs have incorporated viewpoints of patients and health care professionals to determine readmission preventability or to prioritize opportunities for care improvement. OBJECTIVES: To determine preventability of readmissions and to use these estimates to prioritize areas for improvement. DESIGN, SETTING, AND PARTICIPANTS: An observational study was conducted of 1000 general medicine patients readmitted within 30 days of discharge to 12 US academic medical centers between April 1, 2012, and March 31, 2013. We surveyed patients and physicians, reviewed documentation, and performed 2-physician case review to determine preventability of and factors contributing to readmission. We used bivariable statistics to compare preventable and nonpreventable readmissions, multivariable models to identify factors associated with potential preventability, and baseline risk factor prevalence and adjusted odds ratios (aORs) to determine the proportion of readmissions affected by individual risk factors. MAIN OUTCOME AND MEASURE: Likelihood that a readmission could have been prevented. RESULTS: The study cohort comprised 1000 patients (median age was 55 years). Of these, 269 (26.9%) were considered potentially preventable. In multivariable models, factors most strongly associated with potential preventability included emergency department decision making regarding the readmission (aOR, 9.13; 95% CI, 5.23-15.95), failure to relay important information to outpatient health care professionals (aOR, 4.19; 95% CI, 2.17-8.09), discharge of patients too soon (aOR, 3.88; 95% CI, 2.44-6.17), and lack of discussions about care goals among patients with serious illnesses (aOR, 3.84; 95% CI, 1.39-10.64). The most common factors associated with potentially preventable readmissions included emergency department decision making (affecting 9.0%; 95% CI, 7.1%-10.3%), inability to keep appointments after discharge (affecting 8.3%; 95% CI, 4.1%-12.0%), premature discharge from the hospital (affecting 8.7%; 95% CI, 5.8%-11.3%), and patient lack of awareness of whom to contact after discharge (affecting 6.2%; 95% CI, 3.5%-8.7%). CONCLUSIONS AND RELEVANCE: Approximately one-quarter of readmissions are potentially preventable when assessed using multiple perspectives. High-priority areas for improvement efforts include improved communication among health care teams and between health care professionals and patients, greater attention to patients' readiness for discharge, enhanced disease monitoring, and better support for patient self-management.

Strategies to implement alcohol screening and brief intervention in primary care settings: a structured literature review.

Although alcohol screening and brief intervention (SBI) reduces drinking in primary care patients with unhealthy alcohol use, incorporating SBI into clinical settings has been challenging. We systematically reviewed the literature on implementation studies of alcohol SBI using a broad conceptual model of implementation, the Consolidated Framework for Implementation Research (CFIR), to identify domains addressed by programs that achieved high rates of screening and/or brief intervention (BI). Seventeen articles from 8 implementation programs were included; studies were conducted in 9 countries and represented 533,903 patients (127,304 patients screened), 2,001 providers, and 1,805 clinics. Rates of SBI varied across articles (2-93% for screening and 0.9-73.1% for BI). Implementation programs described use of 7-25 of the 39 CFIR elements. Most programs used strategies that spanned all 5 domains of the CFIR with varying emphases on particular domains and sub-domains. Comparison of SBI rates was limited by most studies' being conducted by 2 implementation programs and by different outcome measures, scopes, and durations. However, one implementation program reported a high rate of screening relative to other programs (93%) and could be distinguished by its use of strategies that related to the Inner Setting, Outer Setting, and Process of Implementation domains of the CFIR. Future studies could assess whether focusing on Inner Setting, Outer Setting, and Process of Implementation elements of the CFIR during implementation is associated with successful implementation of alcohol screening, as well as which elements may be associated with successful, sustained implementation of BI.

Transition from paper to electronic inpatient physician notes.

UW Medicine teaching hospitals have seen a move from paper to electronic physician inpatient notes, after improving the availability of workstations, and wireless laptops and the technical infrastructure supporting the electronic medical record (EMR). The primary driver for the transition was to unify the medical record for all disciplines in one location. The main barrier faced was the time required to enter notes, which was addressed with data-rich templates tailored to rounding workflow, simplified login and other measures. After a 2-year transition, nearly all physician notes for hospitalized patients are now entered electronically, approximately 1500 physician notes per day. Remaining challenges include time for note entry, and the perception that notes may be more difficult to understand and to find within the EMR. In general, the transition from paper to electronic notes has been regarded as valuable to patient care and hospital operations.

Building an automated problem list based on natural language processing: lessons learned in the early phase of development.

Detailed problem lists that comply with JCAHO requirements are important components of electronic health records. Besides improving continuity of care electronic problem lists could serve as foundation infrastructure for clinical trial recruitment, research, biosurveillance and billing informatics modules. However, physicians rarely maintain problem lists. Our team is building a system using MetaMap and UMLS to automatically populate the problem list. We report our early results evaluating the application. Three physicians generated gold standard problem lists for 100 cardiology ambulatory progress notes. Our application had 88% sensitivity and 66% precision using a non-modified UMLS dataset. The systemâs misses concentrated in the group of ambiguous problem list entries (Chi-square=27.12 p<0.0001). In addition to the explicit entries, the notes included 10% implicit entry candidates. MetaMap and UMLS are readily applicable to automate the problem list. Ambiguity in medical documents has consequences for performance evaluation of automated systems.

Interprofessional collaboration and job satisfaction of chiropractic physicians.

BACKGROUND: Despite the fact that chiropractic physicians (DCs) are growing in number and legitimacy in the community of health care professionals, little recent research describes how their relationships with medical doctors (MDs) affect their job and career perceptions. OBJECTIVE: This study explores interprofessional relations by identifying factors associated with variations in how DCs evaluate their interaction with MDs. It also adapts a previously validated multifaceted measure of MD job satisfaction for use with DCs. DESIGN: Cross-sectional survey of 311 DC physicians in North Carolina. RESULTS: The hypothesized multifaceted nature of DC job satisfaction was confirmed. Four distinct job facets and global career satisfaction were measured effectively in DCs. DCs' career satisfaction is related to satisfaction with compensation, intrinsic motivation of relating to patients, and having positive relationships with DC colleagues. DCs report referring patients to MDs more often than they report MDs referring patients to them. Satisfaction with relationships between DCs and MDs is relatively low and is strongly linked to the quantity of referrals from MDs and the perception that MDs practice collaboratively with DCs. However, DCs' global career satisfaction is unrelated to their relationships with MDs. CONCLUSION: Global career satisfaction of DCs is relatively high and unaffected by the low level of satisfaction DCs report having with their relationships with MDs. These findings suggest that despite increasing interaction and interdependence, DCs' relationship with MDs is of minor importance in their professional self-image.