A Case Manager Intervention to Reduce Readmissions

General internists influence students to choose primary care careers: The power of role modeling

e13532 Background: One of the most important cancer centers in Ohio experienced an explosive growth in the number of inpatient admissions, with numerous inpatient services created between 2010 and 2022. In this abstract, we present how some financial and quality measures performed during this rapid expansion. Methods: Slicer-Dicer aggregate data was used to compare financial performance and quality measures of oncologic and general medicine inpatient (IP) services over time. General medicine services do not include the cancer and the heart disease populations, but there might be a small overlap. The cancer population is served by 9 IP oncology services. The following SlicerDicer Filters were used. Population: IP admissions. Slices: populations include these study groups, 10 inpatient medical oncology (solid tumors) (MO), 6 inpatient malignant hematology (MH) (including 1 bone marrow transplant) services were compared to 10 general medicine (GM) services. Gynecologic and central nervous system tumors are served by other services and were excluded. Measures: number of admissions, median and total payments (actual payment received), median and total expected reimbursement (reimbursement expected for DRG), mortality, readmission rates, average length of stay (LOS), percentage of patients with Medicaid, percentage of patients with Medicare. Dates: from 2011 to 2022 data. For some measures, quarterly data is presented rather than yearly data for graph simplicity. Results: The number of inpatient admissions grew by 90.77%, 130.55%, and 105.58% for GM, MO, and MH, respectively. The total expected reimbursement increased by 6.22%, 13.56% and decreased by 17.14% for GM, MO and MH, respectively. The total payment received grew by 314.29%, 283.69%, 86.80%, for MO, MH, and GM, respectively. MO, GM, and HM had a median payment increase of 76.51%, 51.96%, 20.74%, respectively. Mortality dropped for MO by 32.11%, from 76.9% to 52.2%, for MH by 49.46%, from 55.6% to 28.1% and in GM by 73.80% from 35.5 % to 9.3%. The average length of stay was 1 to 2 days longer in the MH and GM (respectively) compared to MO, with an increase from 2011 to 2022 for GM and MO, but a decrease for MH. Readmission rates decreased from 17.4% to 15.1% for MO, 12.7% to 10.3% for MH, and 14.9 to 12.5% for GM. In 2014, the percentage of Medicaid population increased in GM, and decreased in the oncology services (MO and MH) reaching 35% and 14% of the admitted patient population in 2022, respectively. Percentage of Medicare patients increased from 36.9% to 52.9%, 39.6% to 44.9%, and remained stable for MO, HM, and GM, respectively. Hospice discharge fluctuated between 1.2-1.6%, 2.5-5%, 8-12.9% of the discharged patients in GM, MH, and MO, respectively. Conclusions: In this abstract, we present a decade-trend data of important measures of inpatient oncology units.

Exploring Entrustment: Housestaff Autonomy and Patient Readmission

Six-year Documentation of the Association between Excellent Clinical Teaching and Improved Studentsʼ Examination Performances

Organization of Diabetes Care

Automated home monitoring systems have been used to coordinate care to improve patient outcomes and reduce rehospitalizations, but with little formal study of efficacy. The Geisinger Monitoring Program (GMP) interactive voice response protocol is a post-hospital discharge telemonitoring system used as an adjunct to existing case management in a primary care Medicare population to reduce emergency department visits and hospital readmissions. To determine if use of GMP reduced 30-day hospital readmission rates among case-managed patients. A pre-post parallel quasi-experimental study. A total of 875 Medicare patients who were enrolled in the combined case-management and GMP program were compared with 2420 matched control patients who were only case managed. Claims data were used to document an acute care admission followed by a readmission within 30 days in the preintervention and postintervention periods (ie, before and during 2009). Regression modeling was used to estimate the within-patient effect of the intervention on readmission rates. The use of GMP with case management was associated with a 44% reduction in 30-day readmissions in the study cohort (95% confidence interval, 23%-60%, P=0.0004), when using the control group to control for secular trends. Similar estimates were obtained when using different propensity score adjustment methods or different approaches to handling dropout observations. Investing in automated monitoring systems may reduce hospital readmission rates among primary care case-managed patients. Evidence from this quasi-experimental study demonstrates that the combination of telemonitoring and case management, as compared with case management alone, may significantly reduce readmissions in a Medicare Advantage population.

On behalf of all diabetes consultants working in the community—whether employed through primary care or hospital trusts—we are writing in response to the letter in the March issue which suggested that additional training for community consultants was unnecessary.1 We agree that diabetes is the same condition whether it is managed in the hospital or community setting—or in specialist or primary care—but would like to point out a few of the challenges for consultants leading services for people who do not attend traditional ‘secondary care’, and where additional training is helpful to consultants who are considering working in this environment. Strong leadership skills are needed to develop diabetes services in the rapidly changing environment of primary care (e.g. getting to grips with General Medical Services, Quality and Outcomes Framework and Practice Based Commissioning). ‘Our health, Our care, Our say’ encourages most diabetes manage-ment to be undertaken in local community settings or primary care. Clinics in the hospital setting are increasingly focusing on patients with highly complex or subspecialty diabetes needs. The community diabetologist has an important role in leading services for people who do not meet the criteria for such hospital clinics but who have more complex needs than those the GP can manage. The development of high quality services that have ‘economies of scale’ to cope with large numbers of patients (particularly in patient education, group consultations etc) will be essential. Community diabetol-ogists are increasingly working alongside public health specialists, epidemiologists and statisticians on issues such as prevention and increas-ing the ascertainment of diabetes, whilst acquiring knowledge of techniques such as social marketing! Clinically, community consultants are more likely to see patients who are not seen in a hospital clinic—e.g. those who are housebound, living in a nursing home, travellers, and psychiatric patients who all have particular diabetes needs. Community consultants need to develop new ways of working due to the fact that they also see patients who do not attend hospital clinics because the system there does not work for them. Although many hospital-based diabetes care teams have always been involved with the education of GPs and practice nurses, the community consultant's role includes not just delivering training but also ensuring it is embedded into the local diabetes management framework (e.g. Local Enhanced Services) which they have developed. The consultant may be working in environments very different from those of hospital clinics (e.g. commu-nity centres, GP surgeries, mosques, even supermarkets!) with different computer systems and organisation of care. Multidisciplinary team working is as important as it is in secondary care, but there are different levels and disciplines in the community (e.g. local pharmacists, nursing home staff, district nurses, and case managers). Community consultants are an important link between primary and specialist care. Experienced commu-nity diabetes consultants can see diabetes issues from both primary and specialist care perspectives and, with appropriate skills, can facilitate integrated care and partnership in the challenging NHS in which we are all working. Gillian Hawthorne Community Diabetes Consultants committee, Waqar Malik Community Diabetes Consultants committee, Felix Burden Community Diabetes Consultants committee, Chris Walton Community Diabetes Consultants committee, Jill Hill Community Diabetes Consultants committee

NHS." What is clear is that the standards were drafted by one agency (the Department of Health), the criteria and assessment process will be developed by another (the Healthcare Commission),...

Most health care spending is concentrated within a small group of high-cost health care users. To inform health policies, we examined the characteristics of index hospital admissions and their predictors among incident older high-cost users compared to older non-high-cost users in Ontario. Using Ontario administrative data, we identified incident high-cost users aged 66 years or more and matched them 1:3 on age, gender and Local Health Integration Network with non-high-cost users aged 66 years or more. We defined high-cost users as patients within the top 5% most costly high-cost users during fiscal year 2013/14 but not during 2012/13. An index hospital admission, the main outcome, was defined as the first unplanned hospital admission during 2013/14, with no hospital admissions in the preceding 12 months. Descriptively, we analyzed the attributes of index hospital admissions, including costs. We identified predictors of index hospital admissions using stratified logistic regression. Over half (95 375/175 847 [54.2%]) of all high-cost users had an unplanned index hospital admission, compared to 8838/527 541 (1.7%) of non-high-cost users. High-cost users had a poorer health status, longer acute length of stay (mean 7.5 d v. 2.9 d) and more frequent designation as alternate level of care before discharge (20.8% v. 1.7%) than did non-high-cost users. Ten diagnosis codes accounted for roughly one-third of the index hospital admission costs in both cohorts. Although many predictors were similar between the cohorts, a lower risk of an index hospital admission was associated with residence in long-term care, attachment to a primary care provider and recent consultation by a geriatrician among high-cost users. The high prevalence of index hospital admissions and the corresponding costs are a distinctive feature of incident older high-cost users. Improved access to specialist outpatient care, home-based social care and long-term care when required are worth further investigation.

Impact of interdisciplinary case management and pharmacist transitions of care interventions on 30-day readmissions

To determine whether raters using the American Board of Internal Medicine (ABIM) Resident Evaluation Form can detect differences among residents in clinical competence. Cross-sectional study. Inpatient general medicine service in a university-affiliated public hospital. University-based internal medicine (UCIM) residents (ABIM certifying examination pass rate, 91%; mean score, 95th percentile), community hospital-based internal medicine (CHIM) residents (ABIM examination pass rate, 68%; mean score, 42nd percentile), and residents from three university-based non-internal medicine (UC non-IM) programs all assigned to the same inpatient general medicine service over a three-year period. Four hundred eighty-nine evaluations of 110 postgraduate-year-one residents were analyzed. Mean ratings for the UCIM residents were significantly higher than those for the CHIM or UC non-IM residents (analysis of variance [ANOVA], p < 0.05). Variance was smallest for the UCIM residents (F test, p < 0.01), and only the UCIM residents' mean scores were in the "superior" range (7-9) in all evaluated categories. The mean ratings for the CHIM residents while at the university-affiliated hospital were not significantly different from the ratings of the same residents at their home hospital. The ratings for the CHIM residents at either site were significantly lower than those for the UCIM residents in all categories (ANOVA, p < 0.05). Factor analysis revealed a single factor accounting for 76% of the variance among the ratings with all dimensions loading high on that factor (0.75-0.95), providing evidence for a "halo" effect. Mean interrater agreement over all variables was 0.87, indicating good consistency among raters. Ratings on the ABIM Resident Evaluation Form detect global differences among residents in clinical competence in the expected direction based on type of training program and performance on the ABIM certification examination, but fail to differentiate among the nine evaluated dimensions of clinical care. This rating method may be valid for assessing overall clinical performance, but is less useful for providing feedback in specific areas to individual residents.

Introduction: Hospitalists have been shown to have shorter lengths of stays than physicians with concurrent outpatient practices. However, hospitalists at academic medical centers may be less aware of local resources that can support the hospital to home transition for local primary care patients. We hypothesized that local family medicine patients admitted to a family medicine inpatient service have shorter length of stay than those admitted to general hospitalist services which also care for tertiary patients at an academic medical center. Methods: A retrospective cohort study was conducted at an academic medical center with a department of family medicine providing primary care to over 80 000 local patients. A total of 3100 consecutive family medicine patients admitted to either the family medicine inpatient service or a general medicine inpatient service over 3 years were studied. The primary outcome was length of stay, which was adjusted using multivariate linear regression for demographics, prior utilization, diagnosis, and disease severity. Results: Adjusted length of stay was 33% longer (95% CI 24%-44%) for local family medicine patients admitted to general medicine inpatient services as compared with the family medicine inpatient service. Readmission rates within 30 days were not different (19% vs 16%, P = .14). Conclusions: Local primary care patients were safely discharged from the hospital sooner on the family medicine inpatient service than on general medicine inpatient services. This is likely because the family physicians staffing their inpatient service are more familiar with outpatient resources that can be effectively marshaled to help local patients with the transition from hospital to home.

The International Journal of Integrated Care (IJIC) is an online, open-access, peer-reviewed scientific journal that publishes original articles in the field of integrated care on a continuous basis.IJIC has an Impact Factor of 5.120 (2020 JCR, received in June 2021)

A multifaceted intervention was hypothesized to increase postdischarge ambulatory contacts and to reduce nonelective readmissions. Patients (N = 1,001) discharged from the general medicine service were stratified by risk for nonelective readmission and randomized to the control or intervention group. Intervention patients received phone calls from nurses, mailings of appointment reminders and lists of early warning signs, and prompt rescheduling of visit failures. Patients were followed for 6 months, and the results were computed in units per patient per month. The intervention group had 10.4% more total office contacts (0.53 vs 0.48, P = 0.005) than the control group. Although the intervention group had 7.6% fewer nonelective readmission days (0.85 vs 0.92), this was not statistically significant (P = 0.5). Patients in the intervention group at high risk (N = 181) had 28.1% more office visits (0.73 vs 0.57, P less than 0.01) and 31.9% fewer nonelective readmission days (1.13 vs 1.66), but this was also not statistically significant (P = 0.06). Thus, the intervention significantly increased post-discharge contacts, primarily in high-risk patients, but failed to reduce the incidence of nonelective readmission days significantly.

BackgroundAn ageing population with multimorbidity is putting pressure on health systems. A popular method of managing this pressure is identification of patients in primary care ‘at-risk’ of hospitalisation, and delivering case management to improve outcomes and avoid admissions. However, the effectiveness of this model has not been subjected to rigorous quantitative synthesis.Methods and FindingsWe carried out a systematic review and meta-analysis of the effectiveness of case management for ‘at-risk’ patients in primary care. Six bibliographic databases were searched using terms for ‘case management’, ‘primary care’, and a methodology filter (Cochrane EPOC group). Effectiveness compared to usual care was measured across a number of relevant outcomes: Health – self-assessed health status, mortality; Cost – total cost of care, healthcare utilisation (primary and non-specialist care and secondary care separately), and; Satisfaction – patient satisfaction. We conducted secondary subgroup analyses to assess whether effectiveness was moderated by the particular model of case management, context, and study design. A total of 15,327 titles and abstracts were screened, 36 unique studies were included. Meta-analyses showed no significant differences in total cost, mortality, utilisation of primary or secondary care. A very small significant effect favouring case management was found for self-reported health status in the short-term (0.07, 95% CI 0.00 to 0.14). A small significant effect favouring case management was found for patient satisfaction in the short- (0.26, 0.16 to 0.36) and long-term (0.35, 0.04 to 0.66). Secondary subgroup analyses suggested the effectiveness of case management may be increased when delivered by a multidisciplinary team, when a social worker was involved, and when delivered in a setting rated as low in initial ‘strength’ of primary care.ConclusionsThis was the first meta-analytic review which examined the effects of case management on a wide range of outcomes and considered also the effects of key moderators. Current results do not support case management as an effective model, especially concerning reduction of secondary care use or total costs. We consider reasons for lack of effect and highlight key research questions for the future.Review ProtocolThe review protocol is available as part of the PROSPERO database (registration number: CRD42014010824).