In the study, it was aimed to evaluate the relationship between health literacy and successful aging in elderly individuals with type 2 diabetes.

This descriptive study was conducted with the participation of 415 elderly patients with type 2 diabetes who presented to the diabetics outpatient clinic between April-September 2021. The study data were collected through Identifying Information Form, Health Literacy Scale, and Successful Aging Scale. In the analysis of the data, descriptive statistics, Pearson correlation analysis, One-Way ANOVA, and student's t test were used.

Health Literacy Scale total mean score of the elderly individuals was found as 55.50 ± 6.08, and their Successful Aging Scale total mean score was determined to be 38.91 ± 2.05. A positive correlation was found between Health Literacy Scale total mean score and Successful Aging Scale total mean score, while a negative relationship was determined between Successful Aging Scale mean score and HbA1c values (p < 0.001).

As a result of the study, it was concluded that elderly patients with type 2 diabetes who had high levels of health literacy had high levels of successful aging as well.

A person with diabetes is subject to developing micro and macrovascular complications and prevention requires an active role from the person. So, health literacy should have a preponderant role in the health of people with diabetes but this link is yet not fully understood. The objective of this study is to understand the relationship between health literacy and the prevalence of complications in people with diabetes mellitus type 2 (DM2).

This is a multicentric transversal observational exploratory study. A survey was conducted with two health literacy instruments, the Medical Term Recognition Test (METER) and Newest Vital Sign (NVS), filled out by people with DM2 coming to consultation in primary health centers in three main regions of Portugal.  Results: In this sample (n=141), 50.6% were male, 41 to 88 years old, and 56% earned more than the minimum wage. Using the METER tool, it was found that 57.4% of the diabetic patients had functional literacy. Adequate literacy was found in 24.1% with the NVS tool. Also with the NVS tool it was found that 36.2% of the sample subjects had s high probability of limited literacy. Utilizing the METER tool, a statistically significant decrease in health literacy was observed in individuals with diabetic complications (p=0.001). There was no significant relation between the presence of diabetic complications and present blood pressure values, low-density lipoprotein, and socioeconomic index.

In this study, we found a significant relation between lower health literacy and the presence of diagnosed DM2 complications (p=0.001).

Diabetes and hyperglycemia are associated with an increased risk of in-hospital complications that lead to longer lengths of stay, increased morbidity, higher mortality, and risk of readmission. Diabetes care and education specialists (DCESs) working in hospital settings are uniquely prepared and credentialed to serve as content experts to facilitate change and implement processes and programs to improve glycemic-related outcomes. A recent survey of DCESs explored the topic of productivity and clinical metrics. Outcomes highlighted the need to better evaluate the impact and value of inpatient DCESs, advocate for the role, and to expand diabetes care and education teams to optimize outcomes. The purpose of this article was to recommend strategies and metrics that can be used to quantify the work of inpatient DCESs and describe how such metrics can help to show the value of the inpatient DCES and assist in making a business case for the role.

There is currently limited guidance from the American Diabetes Association regarding transitions of care for patients with diabetes.

This study's aim was to determine the impact of a diabetes-specific transitions of care clinic (TOCC) on hospital utilization and patient outcomes in recently discharged patients with diabetes.

This retrospective study evaluated patients seen by TOCC as compared with similar patients discharged from the study institution the year prior. The primary outcome was a composite of the number of unique patients with readmissions/emergency department (ED) visits within 30 days of discharge. Secondary outcomes included a subcomponent analysis of readmissions/ED visits, index hospital length of stay (LOS), and to describe clinical interventions made in clinic. This study was approved by the institutional review board of the Office of Responsible Research Practice at the Ohio State University Wexner Medical Center.

There were 165 patients in the TOCC group and 157 in the control group based on the matching criteria. There was a statistically significant decrease in the primary outcome in the TOCC group versus the control group (18% vs 36%, P < 0.001). In evaluation of its subcomponents, there was a statically significant decrease in patients with readmissions (11% vs 26%, P < 0.001) but not ED visits (10% vs 17%, P = 0.096). The LOS for the TOCC group was shorter at 4 days versus 5 days in the control group (P = 0.055).

The implementation of a diabetes-specific TOCC can decrease both readmissions and ED visits and may impact hospital LOS. In addition, a TOCC can be used to identify gaps in preventive care. The results from this study may help support the creation of similar TOCC at other institutions.

Congestive heart failure (CHF) readmissions are frequent and costly but preventable. The purpose of this study was to analyze socioeconomic and health-related factors of CHF readmissions by examining the relationship between 30-day readmissions of individuals with CHF and their payer status, race, ethnicity, primary language spoken, living arrangement, and comorbidities. This retrospective case-control study used secondary data from 450 CHF patients admitted to a not-for-profit Northern Virginia hospital from July 2014 to December 2017. Data were analyzed using χ 2 and logistic regression. Living arrangements and comorbid chronic renal failure (CRF) were statistically significant predictors of CHF readmissions; all other factors were nonsignificant. Patients who lived with family and those in assisted living facilities were less likely to be readmitted than those who lived alone (odds ratio [OR] = 0.2 and 0.5, respectively). Patients without CRF were less likely to be readmitted than those who had CRF (OR = 0.6). This study contributes data to inform community-based health programs tailored toward frequently readmitted individuals due to CHF exacerbation.

To review and synthesise the current literature on social support and hospital readmission rates.

Hospital readmission rates have not declined significantly since 2010 despite efforts to identify and implement strategies to reduce readmissions. After discharge, patients often report the need for help at home with personal care, medical care and/or transportation. Social factors can positively or negatively affect the transition from hospital to home and the extended recovery period experienced by patients.

Published primary studies in peer-reviewed journals, written in English, assessing the adult medical/surgical population and discussing social support and hospital readmission rates were included. A Preferred Reporting Items for Systematic reviews and Meta-Analyses extension for Scoping Reviews (PRISMA-ScR) was completed for this scoping review.

The search resulted in 2919 articles. After removing duplicates and reviewing content for the inclusion and exclusion criteria, 23 articles were selected for review. Social support is provided by those within one's social circle. There are several types of social support and depending on the needs to the patient, the type of social required and provided is different.

The most common form of social support needed at home for people recovering after a hospitalisation was instrumental support, tangible care in the form of assistance with daily personal and medical care, and transportation. Patients who lacked adequate social support after discharge were at an increased risk of hospital readmission.

Identifying factors, such as social support, that may impact hospital readmission rates is important for quality hospital to home care transitions. Assessing patients' needs and available social support to meet those needs may be an essential part of the discharge planning process to decrease the risk of hospital readmission.

To evaluate the relationship between health literacy, social support, and self-efficacy as predictors of change in A1c and readmission among hospitalized patients with type 2 diabetes (T2D).

This is a secondary analysis of patients with T2D (A1c >8.5%) enrolled in a randomized trial in which health literacy (Newest Vital Sign), social support (Multidimensional Scale of Perceived Social Support), and empowerment (Diabetes Empowerment Scale-Short Form) was assessed at baseline. Multivariable models evaluated whether these concepts were associated with A1c reduction at 12 weeks (absolute change, % with >1% reduction, % reaching individualized target) and readmission (14 and 30 days).

A1c (N=108) decreased >1% in 60%, while individualized A1c target was achieved in 31%. After adjustment for baseline A1c and potential confounders, health literacy was associated with significant reduction in A1c (Estimate -0.21, 95% CI -0.40, -0.01, p=0.041) and >1% decrease in A1c (OR 1.37, 95% CI 1.08, 1.73, p=0.009). However, higher social support was associated with greater adjusted odds of reaching the individualized A1c target (OR 1.63, 95% CI 1.04, 2.55, p=0.32). Both higher empowerment (OR 0.23, 95% CI 0.08, 0.64, p=0.005) and social support (OR 0.57, 95% CI 0.36, 0.91, p=0.018) were associated with fewer readmissions by 14 days, but not 30 days.

The study indicates that health literacy and social support may be important predictors of A1c reduction post-discharge among hospitalized patients with T2D. Social support and diabetes self-management skills should be addressed and early follow-up may be critical for avoiding readmissions.

NCT03455985.

Although the use of electronic order sets has become standard practice for inpatient diabetes management, there is limited decision support at discharge.

In this study, we assessed whether an electronic discharge order set (DOS) plus nurse follow-up calls improve discharge orders and postdischarge outcomes among hospitalized patients with type 2 diabetes mellitus.

This was a randomized, open-label, single center study that compared an electronic DOS and nurse phone calls to enhanced standard care (ESC) in hospitalized insulin-requiring patients with type 2 diabetes mellitus. The primary outcome was change in glycated hemoglobin (HbA1c) level at 24 weeks after discharge. The secondary outcomes included the completeness and accuracy of discharge prescriptions related to diabetes.

This study was stopped early because of feasibility concerns related to the long-term follow-up. However, 158 participants were enrolled (DOS: n=82; ESC: n=76), of whom 155 had discharge data. The DOS group had a greater frequency of prescriptions for bolus insulin (78% vs 44%; P=.01), needles or syringes (95% vs 63%; P=.03), and glucometers (86% vs 36%; P<.001). The clarity of the orders was similar. HbA1c data were available for 54 participants in each arm at 12 weeks and for 44 and 45 participants in the DOS and ESC arms, respectively, at 24 weeks. The unadjusted difference in change in HbA1c level (DOS - ESC) was -0.6% (SD 0.4%; P=.18) at 12 weeks and -1.1% (SD 0.4%; P=.01) at 24 weeks. The adjusted difference in change in HbA1c level was -0.5% (SD 0.4%; P=.20) at 12 weeks and -0.7% (SD 0.4%; P=.09) at 24 weeks. The achievement of the individualized HbA1c target was greater in the DOS group at 12 weeks but not at 24 weeks.

An intervention that included a DOS plus a postdischarge nurse phone call resulted in more complete discharge prescriptions. The assessment of postdischarge outcomes was limited, owing to the loss of the long-term follow-up, but it suggested a possible benefit in glucose control.

ClinicalTrials.gov NCT03455985; https://clinicaltrials.gov/ct2/show/NCT03455985.

When comorbid patients with diabetes have 30-days Unplanned Readmission (URA), they attract more burdens to the healthcare system due to increased cost of treatment, insurance penalties to hospitals, and unavailable bed spaces for new patients. This paper, therefore, aims to develop a risk stratification and a predictive model for identifying patients at various risk severities of 30-days URA.

Patients records of comorbid patients with diabetes treated with different medications were collected from different hospitals and analysed with Principal Component Analysis (PCA) and Multivariate Logistic Regression (MLR) to determine the probability of 30-days URA, which is classified into very low, low, moderate, high, and very high. The risk classes are later modelled using ANOVA feature selection to identify the optimal predictors and the best random forest (RF) hyperparameters for 30-days URA risk stratification. Synthetic Minority Oversampling Technique (SMOTE) was used to balance the risk classes while employing a10-fold cross-validation.

After analysing 17,933 episodes of comorbid diabetes patients' treatment, 10.71% are identified to have 30-days URA with 61.95% of patients at moderate risk, 35.5% at low risk, 2.25% at very low risk, 0.37% at high risk, and 0.08% at very high risk. The predictive accuracy of RF is: - recall: 0.947 ± 0.035, precision: 0.951 ± 0.033, F1-score: 0.947 ± 0.035, AUC: 0.994 ± 0.007 and Average Precision (AP) of 0.99. The predictive accuracies of the risk classes measured with F1-score are: - very low: 0.985 ± 0.019, low risk: 0.871 ± 0.079, moderate: 0.881 ± 0.093, high: 0.999 ± 0.003, and very high: 1.000 ± 0.00.

This study identified the risk severity of comorbid patients with diabetes treated with different medications, making it easier to identify those that will be prioritized on hospitalization to minimize 30-days URA. By relying on the technique developed, vulnerable patients to 30-days URA can be given better post-discharge monitoring to build critical self-management skills that will minimize the cost of diabetes care and improve the quality of life.

The purpose of this prospective observational cohort study was to examine sex differences in glycemic measures, diabetes-related complications, and rates of postdischarge emergency room (ER) visits and hospital readmissions in non-critically ill, hospitalized patients with diabetes.

Demographic data including age, body mass index, race, blood pressure, reason for admission, diabetes medications at admission and discharge, diabetes-related complications, laboratory data (hematocrit, creatinine, hemoglobin A1c, point-of-care blood glucose measures), length of stay (LOS), and discharge disposition were collected. Patients were followed for 90 days following hospital discharge to obtain information regarding ER visits and readmissions.

120 men and 100 women consented to participate in this study. There were no sex differences in patient demographics, diabetes duration or complications, or LOS. No differences were observed in the percentage of men and women with an ER visit or hospital readmission within 30 (39% vs 33%, p=0.40) or 90 (60% vs 49%, p=0.12) days of hospital discharge. More men than women experienced hypoglycemia prior to discharge (18% vs 8%, p=0.026). More women were discharged to skilled nursing facilities (p=0.007).

This study demonstrates that men and women hospitalized with an underlying diagnosis of diabetes have similar preadmission glycemic measures, diabetes duration, and prevalence of diabetes complications. More men experienced hypoglycemia prior to discharge. Women were less likely to be discharged to home. Approximately 50% of men and women had ER visits or readmissions within 90 days of hospital discharge.

NCT03279627.

Analysis of risk factors can pave the way for reducing unscheduled hospital readmissions and improve resource utilisation.

This was a concurrent nested, mixed method study. Factors associated with patients readmitted within 30 days between 2011 and 2015 at the National University Hospital, Singapore (N=104,496) were examined. Fifty patients were sampled in 2016 to inform an embedded qualitative study. Narrative interviews explored the periods of readmissions and related experiences, contrasted against those of non-readmitted patients.

Neoplastic disease (odds ratio [OR] 1.91, 95% confidence interval [CI] 1.70-2.15), number of discharged medications (5 to 10 medications OR 1.21, 95% CI 1.14-1.29; ≥11 medications OR 1.80, 95% CI 1.66-1.95) and length of stay >7 days (OR 1.46, 95% CI 1.36-1.58) were most significantly associated with readmissions. Other factors including number of surgical operations, subvention class, number of emergency department visits in the previous year, hospital bill size, gender, age, Charlson comorbidity index and ethnicity were also independently associated with hospital readmissions. Although readmitted and non-readmitted patients shared some common experiences, they reported different psychological reactions to their illnesses and viewed hospital care differently. Negative emotions, feeling of being left out by the healthcare team and perception of ineffective or inappropriate treatment were expressed by readmitted patients.

Patient, hospital and system-related factors were associated with readmissions, which may allow early identification of at-risk patients. Qualitative analysis suggested several areas of improvement in care including greater empowerment and involvement of patients in care and decision making.

Acute care re-utilization, i.e., hospital readmission and post-discharge Emergency Department (ED) use, is a significant driver of healthcare costs and a marker for healthcare quality. Diabetes is a major contributor to acute care re-utilization and associated costs. The goals of this paper are to (1) review the epidemiology of readmissions among patients with diabetes, (2) describe models that predict readmission risk, and (3) address various strategies for reducing the risk of acute care re-utilization.

Hospital readmissions and ED visits by diabetes patients are common and costly. Major risk factors for readmission include sociodemographics, comorbidities, insulin use, hospital length of stay (LOS), and history of readmissions, most of which are non-modifiable. Several models for predicting the risk of readmission among diabetes patients have been developed, two of which have reasonable accuracy in external validation. In retrospective studies and mostly small randomized controlled trials (RCTs), interventions such as inpatient diabetes education, inpatient diabetes management services, transition of care support, and outpatient follow-up are generally associated with a reduction in the risk of acute care re-utilization. Data on readmission risk and readmission risk reduction interventions are limited or lacking among patients with diabetes hospitalized for COVID-19. The evidence supporting post-discharge follow-up by telephone is equivocal and also limited. Acute care re-utilization of patients with diabetes presents an important opportunity to improve healthcare quality and reduce costs. Currently available predictive models are useful for identifying higher risk patients but could be improved. Machine learning models, which are becoming more common, have the potential to generate more accurate acute care re-utilization risk predictions. Tools embedded in electronic health record systems are needed to translate readmission risk prediction models into clinical practice. Several risk reduction interventions hold promise but require testing in multi-site RCTs to prove their generalizability, scalability, and effectiveness.

Non-Hispanic Black (NHB) and Hispanic/Latinx (H/L) patients bear a disproportionate burden of type 2 diabetes and associated complications. Regular visits to a primary care doctor or diabetes specialist are warranted to maintain glycemic control, but for a myriad of reasons disparity populations may have difficulties receiving diabetes care. We seek to determine the feasibility of telehealth added to care as usual and secondarily to improve health outcomes (hemoglobin A1c [HbA1c]) in NHB and H/L with uncontrolled type 2 diabetes managed with two or three noninsulin agents.

Twenty-nine patients were randomized to monthly phone calls or weekly to biweekly telehealth visits. Feasibility outcomes were summarized descriptively for the telehealth arm. Differences scores for A1C level and surveys were computed between baseline and three months and compared across arms using a two-sample t test or Mann-Whitney U test.

Patients in the telehealth arm completed a median of eight visits (IQR: 5, 8), and 53% of those in the telephone arm completed 100% of their calls. Change in HbA1c was greater for those in the telephone arm (-2.57 vs -2.07%, P = .70) but the mean baseline HbA1c was higher in the telephone group (11.1% vs 10.3%). Although the change in HbA1c was not statistically different across arms, it was clinically significant.

Augmenting care as usual with telehealth provided by telephone or tablet can be of benefit in improving glycemic control in NHB and H/L with type 2 diabetes. Larger studies need to explore this further.

The primary objective of this study was to examine the patient comprehension of diabetes self-management instructions provided at hospital discharge as an associated risk of readmission.

Noncritically ill patients with diabetes completed patient comprehension questionnaires (PCQ) within 48 hours of discharge. PCQ scores were compared among patients with and without readmission or emergency department (ED) visits at 30 and 90 days. Glycemic measures 48 hours preceding discharge were investigated. Diabetes Early Readmission Risk Indicators (DERRIs) were calculated for each patient.

Of 128 patients who completed the PCQ, scores were similar among those with 30-day (n = 31) and 90-day (n = 54) readmission compared with no readmission (n = 72) (79.9 ± 14.4 vs 80.4 ± 15.6 vs 82.3 ± 16.4, respectively) or ED visits. Clarification of discharge information was provided for 47 patients. PCQ scores of 100% were achieved in 14% of those with and 86% without readmission at 30 days (P = .108). Of predischarge glycemic measures, glycemic variability was negatively associated with PCQ scores (P = .035). DERRIs were significantly higher among patients readmitted at 90 days but not 30 days.

These results demonstrate similar PCQ scores between patients with and those without readmission or ED visits despite the need for corrective information in many patients. Measures of glycemic variability were associated with PCQ scores but not readmission risk. This study validates DERRI as a predictor for readmission at 90 days.

While studies have reported factors affecting adherence to diabetic care plans from a chronic disease perspective, no studies have addressed issues with post-discharge adherence facing patients with diabetes after an emergency department (ED) presentation for hyperglycaemia. This study's objectives were to describe patient perspectives on their experience during and after an ED visit for hyperglycaemia and to identify factors that influence postdischarge adherence.

We conducted a qualitative description (QD) study of adult patients who had visited a Canadian ED for hyperglycaemia. Consistent with QD, purposive sampling was utilised, seeking diversity across age, gender and diabetes type. Participants took part in semistructured interviews and thematic analysis was used to identify and describe core themes. Frequent team meetings were held to review the analysis and to develop the final list of themes used to recode the data set. Analytic insights were tracked using reflective memos and an audit trail documented all steps and decisions.

22 patients with type 1 and 2 diabetes were interviewed from June to October 2019. Participants identified several factors that impacted their ability to adhere to discharge plans: communication of instructions, psychosocial factors (financial considerations, shame and guilt, stigma and mental health), access to follow-up care and paediatric to adult care transitions.

This study describes the patient experience with the communication of discharge instructions, as well as factors affecting adherence post-ED discharge for hyperglycaemia. Our findings suggest four strategies that could improve the patient experience, improve adherence to discharge plans and potentially decrease the frequency of recurrent ED visits for hyperglycaemia.

To evaluate the association between inpatient glycemic control and readmission in individuals with diabetes and hyperglycemia (DM/HG).

Two data sets were analyzed from fiscal years 2011 to 2013: hospital data using the International Classification of Diseases, Ninth Revision (ICD-9) codes for DM/HG and point of care (POC) glucose monitoring. The variables analyzed included gender, age, mean, minimum and maximum glucose, along with 4 measures of glycemic variability (GV), standard deviation, coefficient of variation, mean amplitude of glucose excursions, and average daily risk range.

Of 66 518 discharges in FY 2011-2013, 28.4% had DM/HG based on ICD-9 codes and 53% received POC monitoring. The overall readmission rate was 13.9%, although the rates for individuals with DM/HG were higher at 18.9% and 20.6% using ICD-9 codes and POC data, respectively. The readmitted group had higher mean glucose (169 ± 47 mg/dL vs 158 ± 46 mg/dL, P < .001). Individuals with severe hypoglycemia and hyperglycemia had the highest readmission rates. All 4 GV measures were consistent and higher in the readmitted group.

Individuals with DM/HG have higher 30-day readmission rates than those without. Those readmitted had higher mean glucose, more extreme glucose values, and higher GV. To our knowledge, this is the first report of multiple metrics of inpatient glycemic control, including GV, and their associations with readmission.

Hospitals generate large amounts of data on a daily basis, but most of the time that data is just an overwhelming amount of information which never transitions to knowledge. Through the application of Data Mining techniques it is possible to find hidden relations or patterns among the data and convert those into knowledge that can further be used to aid in the decision-making of hospital professionals. This study aims to use information about patients with diabetes, which is a chronic (long-term) condition that occurs when the body does not produce enough or any insulin. The main purpose is to help hospitals improve their care with diabetic patients and consequently reduce readmission costs. An hospital readmission is an episode in which a patient discharged from a hospital is admitted again within a specified period of time (usually a 30 day period). This period allows hospitals to verify that their services are being performed correctly and also to verify the costs of these re-admissions. The goal of the study is to predict if a patient who suffers from diabetes will be readmitted, after being discharged, using Machine Leaning algorithms. The final results revealed that the most efficient algorithm was Random Forest with 0.898 of accuracy.

Diabetes is one of the most common chronic conditions in the United States and has a cost burden over $120 billion per year. Readmissions following hospitalization for diabetes are common, particularly in minority patients, who experience greater rates of complications and lower quality healthcare compared to white patients. This study examines disparities in diabetes-related readmissions in the Military Health System, a universally insured, population of 9.5 million beneficiaries, who may receive care from military (direct care) or civilian (purchased care) facilities.

The study identified a population of 7,605 adult diabetic patients admitted to the hospital in 2014. Diagnostic codes were used to identify hospital readmissions, and logistic regression was used to analyze associations among race, beneficiary status, patient or sponsor's rank, and readmissions at 30, 60, and 90 days.

A total of 239 direct care patients and 545 purchased care patients were included in our analyses. After adjusting for age and sex, we found no significant difference in readmission rates for black versus white patients; however, we found a statistically significant increase in the likelihood for readmission of Native American/Alaskan Native patients compared to white patients, which persisted in direct care at 60 days (adjusted odds ratio [AOR] 11.51, 95% CI 1.11-119.41) and 90 days (AOR 18.42, 95% CI 1.78-190.73), and in purchased care at 90 days (AOR 4.54, 95% CI 1.31-15.74).

Our findings suggest that universal access to healthcare alleviates disparities for black patients, while Native America/Alaskan Native populations may still be at risk of disparities associated with readmissions among diabetic patients in both the closed direct care system and the civilian fee for service purchased care system.

The significance of medication therapy in managing comorbid diabetes is vital for maintaining the overall wellness of patients and reducing the cost of healthcare. Thus, using appropriate medication or medication combinations will be necessary for improved person-centred care and reduce complications associated with diagnosis and treatment. This study explains an intelligent decision support framework for managing 30 days unplanned readmission (30_URD) of comorbid diabetes using the Random Forest (RF) algorithm and Bayesian Network (BN) model. After the analysis of the medical records of 101,756 de-identified diabetic patients treated with 21 medications for 28 comorbidity combinations, the optimal medications for minimizing the likelihood of early readmissions were determined. This approach can help for identifying and managing most vulnerable patients thereby giving room to enhance post-discharge monitoring through clinical specialist supports to build critical-self management skills that will minimize the cost of diabetes care.

The positive impact on patient comprehension and improved procedural outcomes when multimedia is utilized to convey instructions preprocedurally has been previously shown for gastrointestinal procedures such as colonoscopy. However, in gastroesophageal reflux testing (GERD), we continue to utilize verbal and written instructions to establish this diagnosis when we use BRAVO pH testing. This is arguably a more complex procedure involving stopping medications, placement of a device, and maintaining an accurate diary for the duration of the testing. We hypothesize that by utilizing multimedia to relay complex textual information, patients will have improved comprehension of periprocedural instructions thereby improving data entry and satisfaction of expectations during the procedure. Prospective randomized study of 120 patients undergoing endoscopic placement of the BRAVO pH monitoring capsule for evaluation of GERD receive either written preoperative instructions (control) or written plus video instructions (video group). A composite comprehension score was calculated using procedure-specific parameters of data entry over the 48-hour monitoring period. Patient satisfaction was evaluated on the basis of a five-point Likert scale. Extent of patient satisfaction was defined by the fulfillment of patient expectations. Exclusion criteria included patients who did not have access to the video or did not complete follow-up. Seventy-eight patients completed all follow-up evaluations. The video group (n = 44) had a significantly higher mean comprehension score when compared to the control group (n = 34) (9.6 ± 1.4 vs. 7.4 ± 2.0, P = 0.01). Overall satisfaction with instructions was significantly higher in the intervention group (91% vs. 47%, p 0.01). We detected no significant difference in comprehension or satisfaction scores in subgroup analyses of the video group comparing patients <65 and ≥65 years of age and by education level. Compared to standard written instructions, video instructions improved patient comprehension based on data evaluation, and satisfaction. Therefore, clinicians should consider incorporation of multimedia instructions to enhance patient periprocedural expectations and understanding of reflux pH testing using the BRAVO procedure.

Patient-centred care is tailored to the needs of patients and is necessary for better health outcomes, especially for individuals with limited health literacy (LHL). However, its implementation remains challenging. The key to effectively address patient-centred care is to include perspectives of patients with LHL within the curricula of (future) healthcare providers (HCP). This systematic review aimed to explore and synthesize evidence on the needs, experiences and preferences of patients with LHL and to inform an existing educational framework. We searched three databases: PsychInfo, Medline and Cinahl, and extracted 798 articles. One-hundred and three articles met the inclusion criteria. After data extraction and thematic synthesis, key themes were identified. Patients with LHL and chronic diseases encounter multiple problems in the care process, which are often related to a lack of person-centeredness. Patient perspectives were categorized into four key themes: (1) Support system; (2) Patient self-management; (3) Capacities of HCPs; (4) Barriers in healthcare systems. "Cultural sensitivity" and "eHealth" were identified as recurring themes. A set of learning outcomes for (future) HCPs was developed based on our findings. The perspectives of patients with LHL provided valuable input for a comprehensive and person-centred educational framework that can enhance the relevance and quality of education for (future) HCPs, and contribute to better person-centred care for patients with LHL.

Patients with diabetes are increasingly common in hospital settings where optimal glycemic control remains challenging. Inpatient technology-enabled support systems are being designed, adapted and evaluated to meet this challenge. Insulin pump use, increasingly common in outpatients, has been shown to be safe among select inpatients. Dedicated pump protocols and provider training are needed to optimize pump use in the hospital. Continuous glucose monitoring (CGM) has been shown to be comparable to usual care for blood glucose surveillance in intensive care unit (ICU) settings but data on cost effectiveness is lacking. CGM use in non-ICU settings remains investigational and patient use of home CGM in inpatient settings is not recommended due to safety concerns. Compared to unstructured insulin prescription, a continuum of effective electronic medical record-based support for insulin prescription exists from passive order sets to clinical decision support to fully automated electronic Glycemic Management Systems. Relative efficacy and cost among these systems remains unanswered. An array of novel platforms are being evaluated to engage patients in technology-enabled diabetes education in the hospital. These hold tremendous promise in affording universal access to hospitalized patients with diabetes to effective self-management education and its attendant short/long term clinical benefits.

Diabetes self-management education and support improves diabetes-related outcomes, yet less than 50% of persons with diabetes in the USA receive this service. Hospital admissions present a critical opportunity for providing diabetes education. This article presents an overview of the current state of inpatient diabetes education. It incorporates a summary of existing guidance relative to content followed by an overarching discussion of existing inpatient diabetes education models and their reported outcomes, when available.

As diabetes rates continue to soar and adults with diabetes continue to have high hospitalization and readmission rates, hospitals face challenges in assessing and meeting diabetes patients' educational needs. The consensus recommendation for inpatient diabetes teaching is to provide survival skills education to enable safe self-management following discharge until more comprehensive outpatient education can be provided. Established and emerging models for delivery of diabetes survival skills education in the hospital may be broadly grouped as diabetes-specialty care models, diabetes non-specialty care models, and technology-supported diabetes education. These models are often shaped by the availability of diabetes specialists, including endocrinologists and diabetes educators-or lack thereof, and staffing resources for provision of services. Recent studies suggest that all three approaches can be deployed successfully if well planned. This article presents an overview of the current state of inpatient diabetes education. It incorporates a summary of existing guidance relative to content followed by an overarching discussion of existing inpatient diabetes education models and their reported outcomes, when available. The authors seek to make the reader aware of the heterogeneous approaches that are being implemented nationwide for inpatient diabetes education delivery. Meeting inpatient diabetes educational needs will require a sustained effort, diverse strategies based on resources available, and additional research to explore the impact of these strategies on outcomes.

We have limited understanding of which risk factors contribute to increased readmission rates amongst people discharged from hospital with diabetes. We aim to complete the first review of its kind, to identify, in a systematic way, known risk factors for hospital readmission amongst people with diabetes, in order to better understand this costly complication.

The review was prospectively registered in the PROSPERO database. Risk factors were identified through systematic review of literature in PubMed, EMBASE & SCOPUS databases, performed independently by two authors prior to data extraction, with quality assessment and semi-quantitative synthesis according to PRISMA guidelines.

Eighty-three studies were selected for inclusion, predominantly from the United States, and utilising retrospective analysis of local or regional data sets. 76 distinct statistically significant risk factors were identified across 48 studies. The most commonly identified risk factors were; co-morbidity burden, age, race and insurance type. Few studies conducted power calculations; unstandardized effect sizes were calculated for the majority of statistically significant risk factors.

This review is important in assessing the current state of the literature and in supporting development of interventions to reduce readmission risk. Furthermore, it provides an important foundation for development of rigorous, pre-specified risk prediction models.

Starting insulin therapy in hospitalized patients may be associated with an increase in serious adverse events after discharge.

Determine whether post-discharge risks of death and rehospitalization are higher for older hospitalized patients prescribed new insulin therapy compared with oral hypoglycemic agents (OHAs).

Retrospective population-based cohort study including hospital admissions in Ontario, Canada, between April 1, 2004, and Nov 30, 2013.

Persons aged 66 and over discharged after a hospitalization and dispensed a prescription for insulin and/or an OHA within 7 days of discharge. We included 104,525 individuals, subcategorized into four mutually exclusive exposure groups based on anti-hyperglycemic drug use in the 7 days post-discharge and the 365 days prior to the index admission.

Prescriptions at discharge were categorized as new insulin (no insulin before admission), prevalent insulin (prescribed insulin before admission), new OHA(s) (no OHA or insulin before admission), and prevalent OHA (prescribed OHA only before admission) as the referent category. The primary and secondary outcomes were 30-day deaths and emergency department (ED) visits or readmissions respectively.

Of 104,525 patients, 9.2% were initiated on insulin, 4.1% died, and 26.2% had an ED visit or readmission within 30 days of discharge. Deaths occurred in 7.14% of new insulin users, 4.86% of prevalent insulin users, 3.25% of new OHA users, and 3.45% of prevalent OHA users. After adjustment for covariates, new insulin users had a significantly higher risk of death (adjusted hazard ratio (aHR) 1.59, 95% confidence interval (CI) 1.46 to 1.74) and ED visit/readmissions (aHR 1.17, 95% CI 1.12 to 1.22) than prevalent OHA users.

Initiation of insulin therapy in older hospitalized patients is associated with a higher risk of death and ED visits/readmissions after discharge, highlighting a need for better transitional care of insulin-treated patients.

Health literacy is one of the most important determinants of health. Limited health literacy can leads to reduced adherence to treatment, repeated hospitalizations, and increased diseases complications. Several studies on health literacy among Iranian patients with type 2 diabetes have reported different prevalences of health literacy. The present study is aimed to determine through a systematic review and meta-analysis the prevalence of adequate health literacy in the Iranian population. A total of 8 articles that met the inclusion criteria, published from inception until December 2018, were collected. Articles were searched using the following keywords and their possible combinations: Health Literacy, Illiteracy, Functional Health Literacy, Diabetes, Diabetes Mellitus, and Iran. The data were analyzed using meta-analysis and the random-effects model was used to obtain a pooled prevalence estimate along with its 95% confidence interval. Heterogeneity among the studies was assessed using the I2 statistic. Analyses were performed using STATA software, version 12. The overall prevalences of inadequate and borderline health literacy among Iranian patients with type 2 diabetes were 43.47% (95% CI: 31-55.95) and 26.34% (95% CI: 19.49-33.19), respectively. In addition, the prevalence of adequate health literacy among patients with type 2 diabetes was 29.72% (95% CI: 22.79-36.64). There was no significant relationship between health literacy with year of publication, sample size, and patients' age. Inadequate health literacy is high (43.5%) among Iranian patients with type 2 diabetes. This makes it necessary to provide interventions aimed at improving their heath literacy which will reduce hospitalizations and diseases complications.

Diabetes is a growing problem in the United States. Increasing hospital admissions for diabetes patients demonstrate the need for evidence-based care of diabetes patients by inpatient providers, as well as the importance of continuity of care when transitioning patients from inpatient to outpatient providers.

A focused literature review of discharge planning and transitions of care in diabetes, conducted in PubMed is presented. Studies were selected for inclusion based on content focusing on transitions of care in diabetes, risk factors for readmission, the impact of inpatient diabetes education on patient outcomes, and optimal medication management of diabetes during care transitions. American Diabetes Association (ADA) guidelines for care of patients during the discharge process are presented, as well as considerations for designing treatment regimens for a hospitalized patient transitioning to various care settings.

Multiple factors may make transitions of care difficult, including poor communication, poor patient education, inappropriate follow-up, and clinically complex patients. ADA recommendations provide guidance, but an individualized approach for medication management is needed. Use of scoring systems may help identify patients at higher risk for readmission. Good communication with patients and outpatient providers is needed to prevent patient harm. A team-based approach is needed, utilizing the skills of inpatient and outpatient providers, diabetes educators, nurses, and pharmacists.

Structured discharge planning per guideline recommendations can help improve transitions in care for patients with diabetes. A team based, patient-centered approach can help improve patient outcomes by reducing medication errors, delay of care, and hospital readmissions.

In healthcare systems, the cost of unplanned readmission accounts for a large proportion of total hospital payment. Hospital-specific readmission rate becomes a critical issue around the world. Quantification and early identification of unplanned readmission risks will improve the quality of care during hospitalization and reduce the occurrence of readmission. In clinical practice, medical workers generally use LACE score method to evaluate patient readmission risks, but this method usually performs poorly. With this in mind, this study presents a novel method combining support vector machine and genetic algorithm to build the risk prediction model, which simultaneously involves feature selection and the processing of imbalanced data. This model aims to provide decision support for clinicians during the discharge management of patients with diabetes.

The experiments were conducted from a set of 8756 medical records with 50 different features about diabetic readmission. After preprocessing the data, an effective SMOTE-based method was proposed to solve the imbalance data problem. Further, in order to improve prediction performance, a hybrid feature selection mechanism was devised to select the important features. Subsequently, an improved support vector machine-based (SVM-based) method was developed and the genetic algorithm was used to tune the sensitive parameter of the algorithm. Finally, the five-fold cross-validation method was applied to compare the performance of proposed method with other methods (LACE score, logistic regression, naïve bayes, decision tree and feed forward neural networks).

Experimental results indicate that the proposed SVM-based method achieves an accuracy of 81.02%, a sensitivity of 82.89%, a specificity of 79.23%, and outperforms other popular algorithms in identifying diabetic patients who may be readmitted.

Our research can improve the performance of clinic decision support systems for diabetic readmission, by which the readmission possibility as well as the waste of medical resources can be reduced.

To investigate the association between ambulatory medication reconciliation and health care utilization in patients with diabetes.

In this retrospective cohort analysis, we studied adults taking at least one diabetes medication treated in primary care practices affiliated with two academic medical centers between 2000 and 2014. We assessed the relationship between the fraction of outpatient diabetes medications reconciled over a 6-month period and the composite primary outcome of combined frequency of emergency department (ED) visits and hospitalizations over the subsequent 6 months.

Among 261,765 reconciliation assessment periods contributed by 31,689 patients, 176,274 (67.3%), 27,775 (10.6%), and 57,716 (22.1%) had all, some, or none of the diabetes medications reconciled, respectively. Patients with all, some, or no diabetes medications reconciled had 0.354, 0.377, and 0.384 primary outcome events per 6 months, respectively (P < 0.0001). In a multivariable analysis adjusted for demographics and comorbidities, having some or all versus no diabetes medications reconciled was associated with a lower risk of the primary outcome (rate ratio 0.94 [95% CI 0.90-0.98; P = 0.0046] vs. 0.92 [0.89-0.95; P < 0.0001], respectively). Introduction of feedback to individual providers was associated with a significant increase in the odds of all diabetes medications being reconciled (2.634 [2.524-2.749]; P < 0.0001).

A higher fraction of reconciled outpatient diabetes medications was associated with a lower frequency of ED visits and hospitalizations. Individual performance feedback could help to achieve more comprehensive medication reconciliation.

Hospital readmission within 30 days of discharge (30-d readmission) is an undesirable outcome. Readmission of patients with diabetes is common and costly. Most of the studies that have examined readmission risk factors among diabetes patients did not include potentially important clinical data.

To provide a more comprehensive understanding of 30-day readmission risk factors among patients with diabetes based on predischarge and postdischarge data.

In this retrospective cohort study, 48 variables were evaluated for association with readmission by multivariable logistic regression.

In total, 17,284 adult diabetes patients with 44,203 hospital discharges from an urban academic medical center between January 1, 2004 and December 1, 2012.

The outcome was all-cause 30-day readmission. Model performance was assessed by c-statistic.

The 30-day readmission rate was 20.4%, and the median time to readmission was 11 days. A total of 27 factors were statistically significant and independently associated with 30-day readmission (P<0.05). The c-statistic was 0.82. The strongest risk factors were lack of a postdischarge outpatient visit within 30 days, hospital length-of-stay, prior discharge within 90 days, discharge against medical advice, sociodemographics, comorbidities, and admission laboratory values. A diagnosis of hypertension, preadmission sulfonylurea use, admission to an intensive care unit, sex, and age were not associated with readmission in univariate analysis.

There are numerous risk factors for 30-day readmission among patients with diabetes. Postdischarge factors add to the predictive accuracy achieved by predischarge factors. A better understanding of readmission risk may ultimately lead to lowering that risk.

This article was originally published with errors that were introduced during the editing process. The corrected version of this article appears below.

To examine the association of health literacy with the number and type of transitional care needs (TCN) among patients being discharged to home.

A cross-sectional analysis of patients admitted to an academic medical center.

Nurses administered the Brief Health Literacy Screen and documented TCNs along 10 domains: caregiver support, transportation, healthcare utilization, high-risk medical comorbidities, medication management, medical devices, functional status, mental health comorbidities, communication, and financial resources.

Among the 384 patients analyzed, 113 (29%) had inadequate health literacy. Patients with inadequate health literacy had needs in more TCN domains (mean = 5.29 vs 4.36; P < 0 .001). In unadjusted analysis, patients with inadequate health literacy were significantly more likely to have TCNs in 7 out of the 10 domains. In multivariate analyses, inadequate health literacy remained significantly associated with inadequate caregiver support (odds ratio [OR], 2.61; 95% confidence interval [CI], 1.37-4.99) and transportation barriers (OR, 1.69; 95% CI, 1.04-2.76).

Among hospitalized patients, inadequate health literacy is prevalent and independently associated with other needs that place patients at a higher risk of adverse outcomes, such as hospital readmission. Screening for inadequate health literacy and associated needs may enable hospitals to address these barriers and improve postdischarge outcomes.

Heart failure (HF) is a common end point for numerous cardiovascular conditions, including coronary artery disease, valvular disease, and hypertension. HF predominantly affects older individuals (aged ≥70 years), particularly those living in developed countries. The pathophysiological sequelae of HF progression have a substantial negative effect on physical function. Diminished physical function in older patients with HF, which is the result of combined disease-related and age-related effects, has important implications on health. A large body of research spanning several decades has demonstrated the safety and efficacy of regular physical activity in improving outcomes among the HF population, regardless of age, sex, or ethnicity. However, patients with HF, especially those who are older, are less likely to engage in regular exercise training compared with the general population. To improve initiation of regular exercise training and subsequent long-term compliance, there is a need to rethink the dialogue between clinicians and patients. This Review discusses the need to improve physical function and exercise habits in patients with HF, focusing on the older population.

To develop and validate a tool that predicts 30d readmission risk of patients with diabetes hospitalized for cardiovascular disease (CVD), the Diabetes Early Readmission Risk Indicator-CVD (DERRI-CVD™).

A cohort of 8189 discharges was retrospectively selected from electronic records of adult patients with diabetes hospitalized for CVD. Discharges of 60% of the patients (n=4950) were randomly selected as a training sample and the remaining 40% (n=3219) were the validation sample.

Statistically significant predictors of all-cause 30d readmission risk were identified by multivariable logistic regression modeling: education level, employment status, living within 5miles of the hospital, pre-admission diabetes therapy, macrovascular complications, admission serum creatinine and albumin levels, having a hospital discharge within 90days pre-admission, and a psychiatric diagnosis. Model discrimination and calibration were good (C-statistic 0.71). Performance in the validation sample was comparable. Predicted 30d readmission risk was similar in the training and validation samples (38.6% and 35.1% in the highest quintiles).

The DERRI-CVD™ may be a valid tool to predict all-cause 30d readmission risk of patients with diabetes hospitalized for CVD. Identifying high-risk patients may encourage the use of interventions targeting those at greatest risk, potentially leading to better outcomes and lower healthcare costs.

Patients who present to the emergency department (ED) for diabetes without hyperglycemic crisis are at risk of unnecessary hospitalizations and poor outcomes. To address this, the ED Diabetes Rapid-referral Program (EDRP) was designed to provide ED staff with direct booking into the diabetes center. The objective of this study was to determine the effects of the EDRP on hospitalization rate, ED utilization rate, glycemic control, and expenditures.

We conducted a single-center analysis of the EDRP cohort (n = 420) and compared 1-year outcomes to historic controls (n = 791). We also compared EDRP patients who arrived (ARR) to those who did not show (NS). The primary outcome was hospitalization rate over 1 year. Secondary outcomes included ED recidivism rate, hemoglobin A1c (HbA1c), and healthcare expenditures.

Compared with controls, the EDRP cohort was less likely to be hospitalized (27.1% vs. 41.5%, P<.001) or return to the ED (52.2% vs. 62.3%, P = .001) at the end of 1 year. Total hospitalizations were also lower in the EDRP (157 ± 19 vs. 267 ± 18 per 1,000 persons per year, P<.001). The EDRP cohort had a greater reduction in HbA1c (-2.66 vs. -2.01%, P<.001), which was more pronounced when ARR patients were compared with NS (-2.71% vs. -1.37%, P<.05). The mean per patient institutional healthcare expenditures were lower by $5,461 compared with controls.

Eliminating barriers to scheduling diabetes-focused ambulatory care for ED patients was associated with significant reductions in hospitalization rate, ED recidivism rate, HbA1c, and healthcare expenditures in the subsequent year.

ARR = arrived ED = emergency department EDRP = emergency department diabetes rapid-referral Program HbA1c = hemoglobin A1c NS = no show.

The purpose of this review is to provide practical evidence-based recommendations for transitioning hospitalized patients with type 2 diabetes (T2DM) to home.

Hospitalized patients who have newly diagnosed or poorly controlled T2DM require initiation or intensification of their outpatient diabetes regimen. Potential barriers to medication access and continuity of care should be identified early in the hospitalization. Throughout hospitalization, patients should receive diabetes education focused on basic survival skills and tailored to learning needs. Patients should leave the hospital with personalized discharge instructions that include a list of all medications and follow-up appointments with both the outpatient diabetes provider and a diabetes educator whenever possible. An approach to transitioning patients with T2DM from hospital to home that focuses on optimizing the patient's discharge diabetes regimen, anticipating patients' needs during the immediate post-discharge period, providing survival skills education, and ensuring continuation of diabetes care and education following hospital discharge has the potential to improve glycemic control and reduce emergency department visits and hospital readmissions.

The goal of this review is to describe diabetes within a population health improvement framework and to review the evidence for a diabetes population health continuum of intervention approaches, including diabetes prevention and chronic and acute diabetes management, to improve clinical and economic outcomes.

Recent studies have shown that compared to usual care, lifestyle interventions in prediabetes lower diabetes risk at the population-level and that group-based programs have low incremental medial cost effectiveness ratio for health systems. Effective outpatient interventions that improve diabetes control and process outcomes are multi-level, targeting the patient, provider, and healthcare system simultaneously and integrate community health workers as a liaison between the patient and community-based healthcare resources. A multi-faceted approach to diabetes management is also effective in the inpatient setting. Interventions shown to promote safe and effective glycemic control and use of evidence-based glucose management practices include provider reminder and clinical decision support systems, automated computer order entry, provider education, and organizational change. Future studies should examine the cost-effectiveness of multi-faceted outpatient and inpatient diabetes management programs to determine the best financial models for incorporating them into diabetes population health strategies.

Patients 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.

We 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.

We 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.

The Diabetes Resource Nurse program is effective in decreasing readmission rates. Patients seen by the diabetes educators have the lowest rates of readmission.

Scientific evidence is available to guide the how to of medications management when patients with diabetes are hospitalized or present to the Emergency Department. However, few clinical trials in the diabetes field have addressed the execution, coupled with established implementation effectiveness evaluation frameworks to help inform and assess implementation practices to support the transition in care. These deficiencies may be overcome by (1) applying the principles of implementation and delivery systems science; (2) engaging the principles of human factors (HF) throughout the design, development, and evaluation planning activities; and (3) utilizing mixed methods to design the intervention, workflow processes, and evaluate the intervention for sustainability within existing care delivery models. This article provides a discussion of implementation science and human factors science including an overview of commonly used frameworks which can be applied to structure design and implementation of sustainable and generalizable interventions.

To develop and validate a tool to predict the risk of all-cause readmission within 30 days (30-d readmission) among hospitalized patients with diabetes.

A cohort of 44,203 discharges was retrospectively selected from the electronic records of adult patients with diabetes hospitalized at an urban academic medical center. Discharges of 60% of the patients (n = 26,402) were randomly selected as a training sample to develop the index. The remaining 40% (n = 17,801) were selected as a validation sample. Multivariable logistic regression with generalized estimating equations was used to develop the Diabetes Early Readmission Risk Indicator (DERRI™).

Ten statistically significant predictors were identified: employment status; living within 5 miles of the hospital; preadmission insulin use; burden of macrovascular diabetes complications; admission serum hematocrit, creatinine, and sodium; having a hospital discharge within 90 days before admission; most recent discharge status up to 1 year before admission; and a diagnosis of anemia. Discrimination of the model was acceptable (C statistic 0.70), and calibration was good. Characteristics of the validation and training samples were similar. Performance of the DERRI™ in the validation sample was essentially unchanged (C statistic 0.69). Mean predicted 30-d readmission risks were also similar between the training and validation samples (39.3% and 38.7% in the highest quintiles).

The DERRI™ was found to be a valid tool to predict all-cause 30-d readmission risk of individual patients with diabetes. The identification of high-risk patients may encourage the use of interventions targeting those at greatest risk, potentially leading to better outcomes and lower healthcare costs.

DERRI™ = Diabetes Early Readmission Risk Indicator ICD-9-CM = International Classification of Diseases, Ninth Revision, Clinical Modification GEE = generalized estimating equations ROC = receiver operating characteristic.