Justin Marsden

A hospital discharge summary quality improvement program featuring individual and team-based feedback and academic detailing.

Background:Discharge summaries are an important component of hospital care transitions typically completed by interns in teaching hospitals. However, these documents are often not completed in a timely fashion or do not include pertinent details of hospitalization. This report outlines the development and impact of a curriculum intervention to improve the quality of discharge summaries by interns and residents in Internal Medicine. A previous study demonstrated that a discharge summary curriculum featuring individualized feedback was associated with improved summary quality, but few subsequent studies have described implementation of similar curricula. No information exists on the utility of other strategies such as team-based feedback or academic detailing. Methods:Study participants were 96 Internal Medicine intern and resident physicians at an academic medical center-based training program. A comprehensive evidence-based discharge summary quality improvement program was developed and implemented that featured a discharge summary template to facilitate summary preparation, individual feedback, team-based feedback, academic detailing and an objective discharge summary evaluation instrument. Results:The discharge summary evaluation instrument had moderate interrater reliability (&kgr; = 0.72). Discharge summary scores improved from mean score of 70% to 82% (P = 0.05). Interns and residents participating in this program also reported increased confidence in producing and critiquing summaries. Conclusions:A comprehensive discharge summary curriculum can be feasibly implemented within the context of a residency program. Team-based feedback and academic detailing may serve to reinforce individual feedback and extend program reach.

Big Data, Predictive Analytics, and Quality Improvement in Kidney Transplantation: A Proof of Concept

We sought proof of concept of a Big Data Solution incorporating longitudinal structured and unstructured patient‐level data from electronic health records (EHR) to predict graft loss (GL) and mortality. For a quality improvement initiative, GL and mortality prediction models were constructed using baseline and follow‐up data (0–90 days posttransplant; structured and unstructured for 1‐year models; data up to 1 year for 3‐year models) on adult solitary kidney transplant recipients transplanted during 2007–2015 as follows: Model 1: United Network for Organ Sharing (UNOS) data; Model 2: UNOS & Transplant Database (Tx Database) data; Model 3: UNOS, Tx Database & EHR comorbidity data; and Model 4: UNOS, Tx Database, EHR data, Posttransplant trajectory data, and unstructured data. A 10% 3‐year GL rate was observed among 891 patients (2007–2015). Layering of data sources improved model performance; Model 1: area under the curve (AUC), 0.66; (95% confidence interval [CI]: 0.60, 0.72); Model 2: AUC, 0.68; (95% CI: 0.61–0.74); Model 3: AUC, 0.72; (95% CI: 0.66–077); Model 4: AUC, 0.84, (95 % CI: 0.79–0.89). One‐year GL (AUC, 0.87; Model 4) and 3‐year mortality (AUC, 0.84; Model 4) models performed similarly. A Big Data approach significantly adds efficacy to GL and mortality prediction models and is EHR deployable to optimize outcomes.

Point of care experience with pneumococcal and influenza vaccine documentation among persons aged ≥65 years: High refusal rates and missing information

Missed opportunities to vaccinate and refusal of vaccine by patients have hindered the achievement of national health care goals. The meaningful use of electronic medical records should improve vaccination rates, but few studies have examined the content of these records. In our vaccine intervention program using an electronic record with physician prompts, paper prompts, and nursing standing orders, we were unable to achieve national vaccine goals, due in large part to missing information and patient refusal.

A Comprehensive View of Frequent Emergency Department Users Based on Data from a Regional HIE.

Objectives A small but significant number of patients make frequent emergency department (ED) visits to multiple EDs within a region. We have a unique health information exchange (HIE) that includes every ED encounter in all hospital systems in our region. Using our HIE we were able to characterize all frequent ED users in our region, regardless of hospital visited or payer class. The objective of our study was to use data from an HIE to characterize patients in a region who are frequent ED users (FEDUs). Methods We constructed a database from a cohort of adult patients (18 years old or older) with information in a regional HIE for a 1-year period beginning in April 2012. Patients were defined as FEDUs (those who made four or more visits during the study period) and non-FEDUs (those who made fewer than four ED visits during the study period). Predictor variables included age, race, sex, payer class, county of residence, and International Classification of Diseases, Ninth Revision codes. Bivariate (&khgr;2) and multivariate (logistic regression) analyses were performed to determine associations between predictor variables and the outcome of being a FEDU. Results The database contained 127,672 patients, 12,293 (9.6%) of whom were FEDUs. Logistic regression showed the following patient characteristics to be significantly associated with the outcome of being a FEDU: age 35 to 44 years; African American race; Medicaid, Medicare, and dual-pay payer class; and International Classification of Diseases, Ninth Revision codes 630 to 679 (complications of pregnancy, childbirth, and puerperium), 780 to 799 (ill-defined conditions), 280 to 289 (diseases of the blood), 290–319 (mental disorders), 680 to 709 (diseases of the skin and subcutaneous tissue), 710 to 739 (musculoskeletal and connective tissue disease), 460 to 519 (respiratory disease), and 520 to 579 (digestive disease). No significant differences were noted between men and women. Conclusions Data from an HIE can be used to describe all of the patients within a region who are FEDUs, regardless of the hospital system they visited. This information can be used to focus care coordination efforts and link appropriate patients to a medical home. Future studies can be designed to learn the reasons why patients become FEDUs, and interventions can be developed to address deficiencies in health care that result in frequent ED visits.

Academic Detailing to Teach Aging and Geriatrics

Geriatric education is a required component of internal medicine training. Work hour rules and hectic schedules have challenged residency training programs to develop and utilize innovative teaching methods. In this study, the authors examined the use of academic detailing as a teaching intervention in their residents’ clinic and on the general medicine inpatient wards to improve clinical knowledge and skills in geriatric care. The authors found that this teaching method enables efficient, directed education without disrupting patient care. We were able to show improvements in medical knowledge as well as self-efficacy across multiple geriatric topics.

Assessing the Burden of Abnormal LFTs and the Role of the Electronic Health Record: A Retrospective Study

Background: Primary care clinicians encounter abnormal liver function tests (LFTs) frequently. This study assesses the prevalence of abnormal LFTs and patient follow‐up patterns in response. Methods: This is a retrospective study from 2007‐2016 of adult patients with abnormal LFTs seen in an internal medicine clinic. The proportion of patients with follow‐up testing and the time (in days) to repeat LFTs were the primary outcomes measured. Results were evaluated before and after the implementation of the institution’s electronic health record (EHR). Results: This study identified a period prevalence for abnormal LFTs of 39%. Of these, 9,545 unique patients met inclusion criteria, with 8,415 patients (88.2%) possessing follow‐up LFTs and no significant difference in the proportion of patients receiving follow‐up by degree of initial abnormality. Median time to follow‐up in mild abnormalities (1‐2 times normal) was 138 days, compared to 21 days for severe abnormalities (>4 times normal, P < 0.0001). Reduced time to repeat testing across all spectrums of abnormality was observed following EHR implementation, but proportions of missing follow‐up did not improve. A multivariable logistic regression model identified younger age, poverty, living over 50 miles from clinic, recent cohort entry and a lower magnitude of abnormality as predictors for missing repeat LFT testing (area under the curve = 0.838 [95% CI: 0.827‐0.849]). Conclusions: Abnormal LFTs were detected in 39% of all patients seen. The degree of LFT abnormality did not influence rates of follow‐up testing, but does appear to play a role in the timing of repeat testing, when obtained. Follow‐up rates did not improve with EHR implementation.

Identification of High Utilization Inpatients on Internal Medicine Services

Background: As healthcare reform moves toward value based care, hospitals must reduce costs. As a first step, here we developed a predictive model to identify high‐cost patients on admission. Methods: We performed a retrospective observational study of 7,571 adults admitted to internal medicine services from July 1, 2013 to June 30, 2014. We compared the top 10% highest cost patients to other patients (controls) and identified clinical variables associated with high inpatient costs. Using logistic regression analyses, we developed a predictive model that could be used on admission to identify potential high utilization patients. Results: In the 757 high utilizer patients, the median total hospital cost was

When Should ED Physicians Use an HIE? Predicting Presence of Patient Data in an HIE.

53,430 ± 60,679 compared to

Chaos to complexity: leveling the playing field for measuring value in primary care

8,431 ± 7,245 in the control group (P < 0.0001). The median length of stay for high utilization patients was 19.5 ± 32.5 days compared to 3.8 ± 3.9 days in the control group (P < 0.001). Variables associated with high utilization included transfer from an outside hospital (odds ratio [OR] = 1.6), admission to the pulmonary or medical intensive care unit (OR = 2.4), admission to cardiology (OR = 1.8), coagulopathy (OR = 2.6) and fluid and electrolyte disorders (OR = 2.1). A multivariate logistic regression model was used to fit a predictive model for high utilizers. The receiver operating characteristics curve of this prediction model yielded an area under the curve of 0.80. Conclusions: High resource utilization patients appear to have a specific phenotype that can be predicted with commonly available clinical variables. Our predictive formula holds promise as a tool that may help ultimately reduce hospital costs.

Palliative Care Consultation Policy Change and Its Effect on Nursesʼ Moral Distress in an Academic Medical Center

Objectives Health information exchanges (HIEs) make possible the construction of databases to characterize patients as multisystem users (MSUs), those visiting emergency departments (EDs) of more than one hospital system within a region during a 1-year period. HIE data can inform an algorithm highlighting patients for whom information is more likely to be present in the HIE, leading to a higher yield HIE experience for ED clinicians and incentivizing their adoption of HIE. Our objective was to describe patient characteristics that determine which ED patients are likely to be MSUs and therefore have information in an HIE, thereby improving the efficacy of HIE use and increasing ED clinician perception of HIE benefit. Methods Data were extracted from a regional HIE involving four hospital systems (11 EDs) in the Charleston, South Carolina area. We used univariate and multivariable regression analyses to develop a predictive model for MSU status. Results Factors associated with MSUs included younger age groups, dual-payer insurance status, living in counties that are more rural, and one of at least six specific diagnoses: mental disorders; symptoms, signs, and ill-defined conditions; complications of pregnancy, childbirth, and puerperium; diseases of the musculoskeletal system; injury and poisoning; and diseases of the blood and blood-forming organs. For patients with multiple ED visits during 1 year, 43.8% of MSUs had ≥4 visits, compared with 18.0% of non-MSUs (P < 0.0001). Conclusions This predictive model accurately identified patients cared for at multiple hospital systems and can be used to increase the likelihood that time spent logging on to the HIE will be a value-added effort for emergency physicians.