Diabetes Case Management Research Articles

Integrating Social Determinants of Health in Machine Learning-Driven Decision Support for Diabetes Case Management: Protocol for a Sequential Mixed Methods Study.

The use of both clinical factors and social determinants of health (SDoH) in referral decision-making for case management may improve optimal use of resources and reduce outcome disparities among patients with diabetes. This study proposes the development of a data-driven decision-support system incorporating interactions between clinical factors and SDoH into an algorithm for prioritizing who receives case management services. The paper presents a design for prediction validation and preimplementation assessment that uses a mixed methods approach to guide the implementation of the system. Our study setting is a large, tertiary care academic medical center in the Deep South of the United States, where SDoH contribute to disparities in diabetes-specific hospitalizations and emergency department (ED) visits. This project will develop an interpretable artificial intelligence model for a population with diabetes using SDoH and clinical data to identify which posthospitalization cases have a higher likelihood of subsequent ED use. The electronic health record data collected for the study include demographics, SDoH, comorbidities, hospitalization-related factors, laboratory test results, and medication use to predict posthospitalization ED visits. Subsequently, a mixed methods approach will be used to validate prediction outcomes and develop an implementation strategy from insights into patient outcomes from case managers, clinicians, and quality and patient safety experts. As of December 2023, we had abstracted data on 174,871 inpatient encounters between January 2018 and September 2023, involving 89,355 unique inpatients meeting inclusion criteria. Both clinical and SDoH data items were included for these patient encounters. In total, 85% of the inpatient visits (N=148,640) will be used for training (learning from the data) and the remaining 26,231 inpatient visits will be used for mixed-methods validation (testing). By integrating a critical suite of SDoH with clinical data related to diabetes, the proposed data-driven risk stratification model can enable individualized risk estimation and inform health professionals (eg, case managers) about the risk of patients' upcoming ED use. The prediction outcome could potentially automate case management referrals, helping to better prioritize services. By taking a mixed methods approach, we aim to align the model with the hospital's specific quality and patient safety considerations for the quality of patient care and the optimization of case management resource allocation. DERR1-10.2196/56049.

Effect of Case Management for Patients with Gestational Diabetes

Objective: To explore the effect of allocating case managers for gestational diabetes patients. Methods: 200 patients with gestational diabetes mellitus from December 2021 to December 2022 were included in this study, and the collection period. They were divided into groups according to the interventions received. Each patient in the observation group was managed by a case manager, while the control group were managed with conventional methods without the supervision of a case manager. There were 100 cases in each group, and the curative effects of the two groups were compared. Results: The fasting blood glucose and 2-hour postprandial blood glucose in the observation group were significantly lower than those of the control group (P < 0.05). The re-admission rate of patients due to poor blood sugar control in the observation group was lower than that in the control group (P < 0.05). The pregnancy outcome of the observation group was better than that of the control group (P < 0.05). Conclusion: Case management of gestational diabetes can not only control the blood glucose of pregnant women, but also improve pregnancy outcomes.

The Impact of Primary Care Practitioner Intervention as an Adjunct to a Diabetes Case Management System.

Objective The objective of this study was to assess whether additional primary care practitioner (PCP) contacts beyond the intake visit are associated with reduced hemoglobin A1c in patients with type 2 diabetes actively engaged in the Kaiser Permanente case management system. Methods This retrospective cohort study using the Kaiser Permanente electronic health record explored the effect of enhanced PCP contact among adult patients with type 2 diabetes actively working with diabetes case managers (defined as ≥ 4 case manager contacts during the study period). Results A total of 837 patients met the inclusion and exclusion criteria. On average, patients with the highest PCP contact, < 7 contacts, had Ac levels 0.53 lower than those in the lowest PCP contact quartile, < 3 contacts (p = 0.0007). A1c decreased an average of 0.20 when the PCP contact quartile was one quartile higher (p = 0.0004). Holding the baseline A1c constant, the A1c decreased an average of 0.15 when the PCP contact quartile was one quartile higher (p = 0.0024). A1c change was significantly correlated with baseline A1c; A1c decreased by 0.64 more as the baseline A1c level increased by 1 (p < 0.0001). Additionally, the A1c level decreased by 0.02 more when patient age increased by 1 (p < 0.0001). Metformin use was associated with a decrease of A1c by 0.40 (p = 0.0057), whereas insulin use was associated with an increase of A1c by 0.29 (p = 0.0280). Conclusion In summary, a significant reduction was observed in A1c in patients with increased PCP contacts. This effect was seen in patients already receiving recommended case manager support.

Sleep duration predicts subsequent long-term mortality in patients with type 2 diabetes: a large single-center cohort study

BackgroundSleep duration is associated with mortality. However, prior studies exploring whether sleep duration predicts subsequent long-term mortality in patients with diabetes are limited. This study aims to examine whether metabolic factors affect the associations between baseline sleep duration and subsequent risks of all-cause, expanded, and non-expanded cardiovascular disease (CVD) mortalities among patients with type 2 diabetes (T2D).MethodsA total of 12,526 T2D patients aged 30 years and older, with a follow-up period ≥ 3 years, were identified from the Diabetes Case Management Program of a medical center in Taiwan. Sleep duration was measured using computerized questionnaires by case managers, and the time frame for this question was 1 month prior to the interview date. Sleep duration in relation to subsequent mortality from all causes, expanded CVD, and non-expanded CVD was examined using Cox proportional hazard models.ResultsWithin 10 years of follow-up, 2918 deaths (1328 CVD deaths and 1590 non-CVD deaths) were recorded. A J-shaped association was observed for all-cause, expanded CVD, and non-expanded CVD mortalities, and the lowest risks were observed for patients with 5–7 h of sleep. The significant joint effects included sleep duration of more or less than 7 h with age ≥ 65 years [adjusted HRs: 4.00 (3.49–4.60)], diabetes duration ≥ 5 years [1.60 (1.40–1.84)], age at diabetes diagnosis ≤ 45 years [1.69 (1.38–2.07)], insulin use [1.76 (1.54–2.03)], systolic blood pressure/diastolic blood pressure > 130/85 mmHg [1.24 (1.07–1.43)], triglyceride ≥ 150 mg/dL [1.38 (1.22–1.56)], HbA1c ≥ 7% [1.31 (1.13–1.52)], and body mass index < 27 kg/m2 [1.31 (1.17–1.45)] for all-cause mortality.ConclusionA J-shaped association was observed between sleep duration and all-cause and expanded CVD mortality, and a sleep duration of 5–7 h had the lowest mortality risk. Sleep duration also showed significant synergistic interactions with diabetes duration but shared an antagonistic interaction with age and obesity.

Lifetime risks of hip fracture in patients with type 2 diabetic: Taiwan Diabetes Study.

The lifetime risks of hip fracture have not been reported across the age spectrum in male adults and female adults with type 2 diabetes. A retrospective cohort study was conducted on 25275 men and 27953 women with type 2 diabetes aged 30-100 years old and participated in the National Diabetes Case Management Program in 2002-2004 in Taiwan. Sociodemographic factors, biomarkers, and comorbidity at the baseline and hip fracture events were analyzed with Cox proportional hazards regression models with age as the time scale. Significant differences in the lifetime risks of hip fracture were observed between men and women with type 2 diabetes. The cumulative lifetime incidences (%) of hip fracture at 50, 60, 65, 70, 75, 80, and 85 years old for men were 0.11, 0.40, 0.84, 1.84, 3.82, 8.53, and 16.72, respectively. The corresponding lifetime incidences (%) for women at 50, 60, 65, 70, 75, 80, and 85 years old were 0.05, 0.50, 1.36, 3.89, 9.56, 21.19, and 35.45, respectively. With competing risks, the significant multivariate-adjusted hazard ratio of developing hip fracture included smoking, alcohol drinking, duration of diabetes, type of oral hypoglycemic drugs use (no medication, sulfonylurea only, thiazolidinediones (TZD) only or TZD plus others, other single or multiple oral agents, insulin use, insulin plus oral hypoglycemic drug use), loop diuretics use, use of corticosteroids, normal weight or underweight, hyperlipidemia, and chronic obstructive pulmonary disease. The gender differences in lifetime hip fracture risk were significant. Thiazolidinediones and insulin use are factors with the greater magnitude of strength of association among those significantly associated with hip fracture.

Outcome of Text messaging on glycemic control in children with type 1 diabetes mellitus: A Single Group Pre and Post intervention Study.

Objectives: The objective of this study is to find the outcome of text messaging on glycemic control in children with type1 DM. Study Design: Prospective Single Group Pre- and Post-intervention study. Setting: Diabetic Clinic of Department of Pediatric Endocrinology at The Children hospital and the Institute of Child Health, (CH &amp; ICH) Multan. Period: October 2019 to December 2019. Material &amp; Methods: Forty nine children and adolescents of 10-15 years old with T1DM were included in this study after taking informed consent from patient or their caregivers. Text messages were sent 5 times per week to patients/parents in simple Urdu language about diabetes education and Interactive message was sent weekly about blood sugar monitoring and insulin dose modification. To make sure patients are receiving text messages, phone calls were made regularly. Results: Among diabetic children 24 (49%) were males. The age ranged from 10-15 years and mean age was 12.6 ± 1.8 years. Mean duration of diabetes was 4.7± 2.8 years. Before intervention fasting and post prandial blood glucose levels per month were 160.4 mg/dl and 190 mg/dl respectively. After 12 weeks of text messaging fasting and post prandial blood glucose levels significantly (p-value &lt; 0.001) dropped to 135.2 mg/dl and 170 mg/dl respectively. Similarly, HbA1C level decreased from 10% to 9% after intervention with p value of &lt;0.001. Similar significant improvement was seen in frequency of hypoglycemic/hyperglycemic episode, missed insulin doses and frequency of hospital admissions. Conclusion: Our study showed a significant improvement in glycemic control and diabetes case management by diabetic educational and interactive text messages.

Applications of Cognitive and Social Development Theory in Pediatric Type 1 Diabetes Case Management

Type 1 Diabetes (T1D) can occur at any age but is most commonly diagnosed between infancy and late 30s. The SEARCH for Diabetes in Youth study estimated that in 2009 about 18,436 US. Youth were newly diagnosed with T1D. Approximately 70% of these youths were non-Hispanic Whites, 17% were Hispanics, and 11% were non-Hispanic Blacks [1]. T1D is associated with an increased risk for several health problems including cardiovascular diseases [2-4], epilepsy [5], retinopathy [6], and thyroid autoimmunity [7,8]. Studies in Norway found that despite improved diabetes care, mortality remains three to four times higher among those with childhood-onset diabetes compared with the general population [9]. When looking at case management as a whole in regard to diabetes, many problems can be seen with the current management plans and limitations can be seen in the methods used to educate diabetics on their condition. When looking at future paths to take to improve diabetes case management a few can be proposed including evidence-based case management, case management methods that address low health literacy and numeracy and using various phycological behavior theories to see how this adolescent population can be best addressed.

Quality of Life Assessment in Diabetic Patients Using a Validated Tool in a Patient Population Visiting a Tertiary Care Center in Bhubaneswar, Odisha, India.

Odisha has 4.2 million diabetic patients against the country's 70 million with an urban prevalence of nearly 15.4%. Diabetes is affecting younger age groups, thus having a crucial impact on quality of life of the affected. A qualitative endeavour was attempted at the diabetic clinic of a tertiary care set up in the capital city of Bhubaneswar to create a diabetic surveillance data assembly, wherein subjects above 18 years of age and newly diagnosed or on follow-up, after obtaining informed consent, were made to respond to a quality of life (QOLID) validated tool. The pretested tool has 8-domain role limitation due to physical health, physical endurance, general health, treatment satisfaction, symptom botherness, financial worries, emotional/mental health, and diet advice tolerance. The validated tool had 34 items (questions) that were selected to represent these domains on the basis of extraction communality, factor loading, and interitem and item-total correlations. The final questionnaire had an overall Cronbach's alpha value of 0.894 (subscale: 0.55 to 0.85), showing high internal consistency in the current study population. A score for each domain was calculated by simple addition of items scores. Each individual domain score was then standardized by dividing by maximum possible domain score and multiplying by 100. All individual standardized domain scores were then added and divided by 8 (number of domain) to obtain an overall score. The data collection was done for 400 patients as an interim analysis. Univariate and subsequently multivariate analysis was performed to decide the predictors that affected quality of life. Age over 50 years (OR = 1.81, CI 1.12–2.93; p=0.014), female gender (OR = 2.05, CI 1.26–3.35; p=0.004), having foot complications (OR = 2.81, CI 1.73–4.55; p < 0.001), and having depression (OR = 1.88, CI 1.15–3.06, p=0.011) emerged as predictors of poor QOLID scores. The tool can be made a subtle part of chronic case management of diabetes to ensure patient's participation in the treatment of the disease and to create a database that can redefine diabetic care in India to suit the diverse regional settings in the country.

Simulation training using WhatsApp (Sim-thru-WhatsApp) improves doctors’ confidence in endocrine and diabetes case management

Simulation is defined as a process that substitutes or amplifies real patient encounters with artificial models, live actors, or virtual reality patients.[1][1] The goal of simulation is to replicate patient care scenarios with guided experiences in a realistic environment.[2][2] This is usually

Competing risk analysis on visit-to-visit glucose variations and risk of depression: The Taiwan Diabetes Study

AimPatients with diabetes have higher rates of depression than does the general population, but diabetes management mainly aims to maintain glucose stability. For this reason, our study assessed the relationship between 1-year variations in fasting plasma glucose (FPG) and risk of depression in Chinese patients with type 2 diabetes (T2D). MethodsThis retrospective cohort study was conducted on 32,829 patients aged ≥30 years who were diagnosed with T2D and who participated in the National Diabetes Case Management Program in Taiwan. Their 1-year FPG variation as a predictor was determined by coefficient of variation (CV), whereas depressive events were analyzed by Cox's proportional hazards models. ResultsDuring a mean 8.23 years of follow-up, 1041 new cases of depression were diagnosed. When patients were grouped based on quartiles of FPG-CV, incidence rates were 3.23, 3.49, 3.96 and 4.80 per 1000 person-years in the first, second, third and fourth quartile subgroups, respectively. After adjusting for traditional risk factors, baseline fasting glucose and HbA1c levels, and diabetes complications, FPG-CV was independently linked with incident depression. Hazard ratios of depression for FPG-CV in the fourth vs first quartile subgroups was 1.33 (95% CI: 1.11–1.59), respectively. ConclusionPatients whose 1-year FPG variations were>42.6% had an increased risk of depression, thus suggesting that FPG variations may be a predictor of depression in patients with T2D. Also, glucose variation during outpatient visits may be an indicator for individualized diabetes management in clinical practice.

670-P: Provider Medication Use Changes for Better and Worse with Education

ADA guidelines for type 2 diabetes recommend metformin (MET) at all A1c levels, combination (COMBO) therapy at higher A1c levels and insulin (INS) for symptoms or A1c &amp;gt; 10. Current provider adherence to guidelines and the ability of educational session to alter that are unclear. Methods: We assessed provider medication use before and after didactic diabetes medication and case management sessions designed to encourage multi-combination therapy and guideline adherence. Surveys asked 163 participants to report preferred drug regimens to be used for 6 patient cases: newly diagnosed (NEW) or established (EST) diabetes patients with A1c of 7.5 (LO), 9.5 (MID) or 10.5 (HI). Results: MET use was remarkably low at higher A1c’s (69% HI, 85% MID, 98% LO) at baseline. After sessions, MET use improved significantly to 84%, 94%, 99%. COMBO (no-INS) was most frequently chosen for MID A1c levels: NEW 67%/EST 43%; compared to 13%/23% for LO 7%/5% HI. In the higher A1c cases, COMBO use increased dramatically with sessions to: 83%/72% MID, 30%/16% HI (all p&amp;lt; 0.005). Use of sulfonylureas decreased for 5 of the 6 cases. Changes in INS use were nuanced. Few providers (2%/4%) chose insulin for LO A1c; this decreased to near 0 after the sessions. INS was used in the majority of patients with HI A1c. INS use decreased significantly in favor of COMBO, not only for MID A1c (38%/51% to 13%/26%), but also for HI (93%/94% to 70%/84%). Summary: Initially, guideline adherence was sub-optimal (underutilization of MET, COMBO and at HI A1c of INS). Short Provider Education sessions improved practices. Use of COMBO’s less likely to cause hypoglycemia increased substantially. However, appropriate INS at HI A1c also decreased. Conclusion: There is a need for more training for better use of the antidiabetic armamentarium by primary care providers. Provider education emphasizing COMBO advantages improves planned prescribing patterns, but careful message tailoring is needed to avoid unintended consequences. Disclosure S. Sarkar: None. C.S. Barnes: None. J. Caudle: None. S.M. John: None. K.P. Clark: None. E. Heiman: None. J.C. Volcy: None. S. Schmidt: None. D.C. Ziemer: None. Funding Sanofi U.S.

REMOVED: Artificial intelligence for diabetes case management: The intersection of physical and mental health

Diabetes is a major public health problem in the United States, affecting roughly 30 million people. Diabetes complications, along with the mental health comorbidities that often co-occur with them, are major drivers of high healthcare costs, poor outcomes, and reduced treatment adherence in diabetes. Here, we evaluate in a large state-wide population whether we can use artificial intelligence (AI) techniques to identify clusters of patient trajectories within the broader diabetes population in order to create cost-effective, narrowly-focused case management intervention strategies to reduce development of complications. This approach combined data from: 1) claims, 2) case management notes, and 3) social determinants of health from ~300,000 real patients between 2014 and 2016. We categorized complications as five types: Cardiovascular, Neuropathy, Opthalmic, Renal, and Other. Modeling was performed combining a variety of machine learning algorithms, including supervised classification, unsupervised clustering, natural language processing of unstructured care notes, and feature engineering. The results showed that we can predict development of diabetes complications roughly 83.5% of the time using claims data or social determinants of health data. They also showed we can reveal meaningful clusters in the patient population related to complications and mental health that can be used to cost-effective screening program, reducing the number of patients to be screened down by 85%. This study outlines creation of an AI framework to develop protocols to better address mental health comorbidities that lead to complications development in the diabetes population. Future work is described that outlines potential lines of research and the need for better addressing the 'people side' of the equation.

Removal notice to "Artificial intelligence for diabetes case management: The intersection of physical and mental health" [IMU (2019) Article number 100191

Removal notice to "Artificial intelligence for diabetes case management: The intersection of physical and mental health" [IMU (2019) Article number 100191

Integrating a diabetes and hypertension case management package within primary health care: a mixed methods feasibility study in Bangladesh

BackgroundAlmost three quarters of non-communicable disease (NCD) deaths, and 82% of premature NCD deaths, occur in low- and middle-income countries. Bangladesh has an estimated 7 million hypertensives and 10 million diabetics, and primary care is struggling to respond. Our aim was to develop and support implementation of a diabetes and hypertension case management package, and assess its appropriateness, feasibility and acceptability in two NCD clinics within two primary-care centres in Bangladesh.MethodsWe used a convergent mixed methods design. We first assessed the level of appropriate hypertension and cardiovascular disease patient management, based on a composite outcome indicator using data from patients’ treatment cards. Appropriate management was primarily informed by International Diabetes Federation (IDF) and World Health Organisation (WHO) guidelines. We then performed qualitative in-depth interviews with doctors and patients to explain these quantitative findings and to understand the challenges to achieving appropriate patient management in the NCD clinics.ResultsEighty-one newly diagnosed patients were recruited. Over 3 months, 53.1% (95% CI 42.3% to 63.6%) of patients were appropriately managed. We found incomplete diagnosis (especially missing hypertension diagnosis alongside diabetes) and non-provision of follow-up appointments were the main causes of the relatively low level of appropriate management. We conducted interviews with 11 patients and 8 health professionals and found a shortage of human resources, reporting materials, available drugs and diagnostic equipment. This undermined patients’ willingness to attend clinics and doctors’ willingness to offer follow-ups. Hands-on skill-building training was valuable in increasing doctors’ competence for appropriate management, but was seen as a novel training method and faced constraints to implementation.ConclusionsA clinical guide, skill-based training and recording package can be implemented in routine primary care and can lead to appropriate management of around half of diabetic and hypertensive patients in a low-income country. However, considerable health systems challenges must be addressed before more patients can be managed appropriately.

Linking household surveys and health facility assessments to estimate intervention coverage for the Lives Saved Tool (LiST)

BackgroundCalls have been made for improved measurement of coverage for maternal, newborn and child health interventions. Recently, methods linking household and health facility surveys have been used to improve estimation of intervention coverage. However, linking methods rely the availability of household and health facility surveys which are temporally matched. Because nationally representative health facility assessments are not yet routinely conducted in many low and middle income countries, estimates of intervention coverage based on linking methods can be produced for only a subset of countries. Estimates of intervention coverage are a critical input for modelling the health impact of intervention scale-up in the Lives Saved Tool (LiST). The purpose of this study was to develop a data-driven approach to estimate coverage for a subset of antenatal care interventions modeled in LiST.MethodsUsing a five-step process, estimates of population level coverage for syphilis detection and treatment, case management of diabetes, malaria infection, hypertensive disorders, and pre-eclampsia, were computed by linking household and health facility surveys. Based on data characterizing antenatal care and estimates of coverage derived from the linking approach, predictive models for intervention coverage were developed. Updated estimates of coverage based on the predictive models were compared, first with current default proxies, then with estimates based on the linking approach. Model fit and accuracy were assessed using three measures: the coefficient of determination, Pearson’s correlation coefficient, and the root mean square error (RMSE).ResultsThe ability to predict intervention coverage was fairly accurate across all interventions considered. Predictive models accounted for 20–63% of the variance in intervention coverages, and correlation coefficients ranged from 0.5 to 0.83. The predictive model used to estimate coverage of management of pre-eclampsia performed relatively better (RMSE = 0.11) than the model estimating coverage of diabetes case management (RMSE = 0.19).ConclusionsThe new approach to estimate coverage represents an improvement over current default proxies in LiST. As the availability of reliable coverage data improves, impact estimates generated by LiST will improve. This study underscores the need for continued efforts to improve coverage measurement, while bringing to the fore the importance of health facility assessments as complementary data sources.

Visit-to-visit glycemic variability is a strong predictor of chronic obstructive pulmonary disease in patients with type 2 diabetes mellitus: Competing risk analysis using a national cohort from the Taiwan diabetes study

BackgroundThis study aims to examine the association between visit-to-visit glucose variability, which was measured by coefficient of variation (CV) of fasting plasma glucose (FPG) and hemoglobin A1c (HbA1c), and risk of chronic obstructive pulmonary disease (COPD) in a large number of patients with type 2 diabetes with an average follow-up of 7.58 years.MethodsWe conducted a retrospective cohort study on 27,257 patients with type 2 diabetes who participated in the National Diabetes Case Management Program in Taiwan. Visit-to-visit variability in HbA1c and FPG at baseline and the incidence of COPD were analyzed using a modified Cox proportional hazards model considering competing risks.ResultsA total of 2,346 incident cases of COPD. Patients were grouped into tertiles of FPG-CV and HbA1c-CV. The incidence rates in the first, second, and third tertiles were 9.87, 11.06, and 13.19, respectively, for FPG-CV and 10.2, 11.81, and 12.07, for HbA1c-CV per 1000 person-years. After adjusting for age, gender, diabetes duration, treatment type, smoking, hypertension, hyperlipidemia, baseline FPG and HbA1c levels, and complications, both FPG-CV and HbA1c-CV were independently associated with COPD. The hazard ratios of COPD for the third terile compared with the first tertile of FPG-CV were 1.26 (95% confidence interval [CI]: 1.13–1.40). Moreover, the hazard ratios of COPD for the third and second tertiles compared with the first tertile of HbA1c-CV were 1.13 (1.02–1.25) and 1.13 (1.02–1.26), respectively.ConclusionsPatients with FPG-CV higher than 34.6% or HbA1c-CV higher than 8.4% exhibited an increased risk of COPD. This finding confirmed the linear relationship of FPG-CV and HbA1c-CV to COPD. Visit-to-visit variability in FPG and HbA1c levels are strong predictors of COPD in patients with type 2 diabetes. Future studies should focus on lung dysfunction in diabetes, and adequate glucose control strategy in regular clinical practices must be established for COPD prevention.

The effect of diabetes case management and Diabetes Resource Nurse program on readmissions of patients with diabetes mellitus

AimsPatients with diabetes have higher readmission rates than those without diabetes, yet limited data on efforts to reduce their readmissions are available. We describe a novel model of inpatient diabetes care, expanding the role of diabetes educators to include case management, and establishment of a Diabetes Resource Nurse program, aimed at increasing the knowledge of staff nurses, and evaluate the impact of this program on readmission rates. MethodsWe performed retrospective analysis of 30-day readmission rates of patients with diabetes before (July 2010–December 2011), and after (January 2012–June 2013) starting the implementation of this tiered inpatient diabetes care delivery model. ResultsWe analyzed 34,472 discharged patient records from the 18-month pre-intervention period, and 32,046 records from the 18-month post-intervention period. The overall 30-day readmission rate for patients with diabetes decreased significantly from 20.1% (pre) to 17.6% (post) intervention (p<0.0001). Patients seen by diabetes educators had the lowest 30-day readmission rates (∼15% during the whole study), a rate approaching the overall hospital readmission rates in those without diabetes in our institution. ConclusionThe Diabetes Resource Nurse program is effective in decreasing readmission rates. Patients seen by the diabetes educators have the lowest rates of readmission.

Clinical and Psychosocial Outcomes of a Structured Transition Program Among Young Adults With Type 1 Diabetes

PurposeWe identified and treated young adults with type 1 diabetes who had been lost to follow-up during their transfer from pediatric to adult care, comparing their clinical, psychosocial, and health care utilization outcomes to participants receiving continuous care (CC) throughout the transition to adult care. MethodsIndividuals in their last year of pediatric care (CC group, n = 51) and individuals lost to follow-up in the transfer to adult care (“lapsed care” [LC] group, n = 24) were followed prospectively for 12 months. All participants were provided developmentally tailored diabetes education, case management, and clinical care through a structured transition program. ResultsAt baseline, LC participants reported lapses in care of 11.6 months. Compared with CC participants, they had higher hemoglobin A1C (A1C; p = .005), depressive symptoms (p = .05), incidence of severe hypoglycemia (p = .005), and emergency department visits (p = .004). At 12-month follow-up, CC and LC participants did not differ on the number of diabetes care visits (p = .23), severe hypoglycemia (no events), or emergency department visits (p = .22). Both groups' A1C improved during the study period (CC: p = .03; LC: p = .02). LC participants' depressive symptoms remained elevated (p = .10), and they reported a decline in life satisfaction (p = .007). There was greater loss to follow-up in the LC group (p = .04). ConclusionsOur study suggests that, for young adults with a history of lapses in care, a structured transition program is effective in lowering A1C, reducing severe hypoglycemia and emergency department utilization, and improving uptake of routine diabetes care. Loss to follow-up and psychosocial concerns remain significant challenges in this population.

Diabetes case management: Improvement measures at Changhua Christian Hospital

Diabetes case management: Improvement measures at Changhua Christian Hospital

The impact of seasonal lifestyle changes on glycemic control in T2DM – Based on Diabetes Case Management Program 2001, Taiwan

The impact of seasonal lifestyle changes on glycemic control in T2DM – Based on Diabetes Case Management Program 2001, Taiwan