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Background Diabetes is a medical and economic burden in the United States. In this study, a machine learning predictive model was developed to predict unplanned medical visits among patients with diabetes, and findings were used to design a clinical intervention in the sponsoring healthcare organization. This study presents a case study of how predictive analytics can inform clinical actions, and describes practical factors that must be incorporated in order to translate research into clinical practice. Methods Data were drawn from electronic medical records (EMRs) from a large healthcare organization in the Northern Plains region of the US, from adult (≥ 18 years old) patients with type 1 or type 2 diabetes who received care at least once during the 3-year period. A variety of machine-learning classification models were run using standard EMR variables as predictors (age, body mass index (BMI), systolic blood pressure (BP), diastolic BP, low-density lipoprotein, high-density lipoprotein (HDL), glycohemoglobin (A1C), smoking status, number of diagnoses and number of prescriptions). The best-performing model after cross-validation testing was analyzed to identify strongest predictors. Results The best-performing model was a linear-basis support vector machine, which achieved a balanced accuracy (average of sensitivity and specificity) of 65.7%. This model outperformed a conventional logistic regression by 0.4 percentage points. A sensitivity analysis identified BP and HDL as the strongest predictors, such that disrupting these variables with random noise decreased the model’s overall balanced accuracy by 1.3 and 1.4 percentage points, respectively. These recommendations, along with stakeholder engagement, behavioral economics strategies, and implementation science principles helped to inform the design of a clinical intervention targeting behavioral changes. Conclusion Our machine-learning predictive model more accurately predicted unplanned medical visits among patients with diabetes, relative to conventional models. Post-hoc analysis of the model was used for hypothesis generation, namely that HDL and BP are the strongest contributors to unplanned medical visits among patients with diabetes. These findings were translated into a clinical intervention now being piloted at the sponsoring healthcare organization. In this way, this predictive model can be used in moving from prediction to implementation and improved diabetes care management in clinical settings.

Background Smoking exacerbates the complications of diabetes, but little is known about whether patients with diabetes who smoke have more unplanned medical visits than those who do not smoke. This study examines the association between smoking status and unplanned medical visits among patients with diabetes. Methods Data were drawn from electronic medical records (EMR’s) from a large healthcare provider in the Northern Plains region of the US, from adult (≥18 years old) patients with type 1 or type 2 diabetes who received care at least once during 2014–16 ( N  = 62,149). The association between smoking status (current, former, or never smoker) and having ≥1 unplanned visit (comprised of emergency department visits, hospitalizations, hospital observations, and urgent care) was examined after adjusting for age, race/ethnicity, and body mass index (BMI). The top ten most common diagnoses for unplanned visits were examined by smoking status. Results Both current and former smoking were associated with an approximately 1.2-fold increase in the odds of having at least one unplanned medical visit in the 3-year period (OR = 1.22, 95% CI = 1.16–129; OR = 1.23, 95% CI = 1.19–1.28, respectively), relative to never-smokers. Most common diagnoses for all patients were pain-related. However, diagnoses related to musculoskeletal system and connective tissue disorders were more common among smokers. Smoking is associated with a higher rate of unplanned medical visits among patients with diabetes in this regional healthcare system. Conclusions Results from this study reveal higher rates of unplanned visits among smokers and former smokers, as well as increased frequencies of unplanned medical visits among current smokers.

Health care systems are increasingly facing challenges posed by the aging of populations. In particular, hospitalization, both initial and subsequent, is often observed among older adult patients. However, research suggests that nearly 23% of all hospitalizations could be avoided. In this perspective, remote patient monitoring (RPM) systems are emerging as a promising solution, enabling professionals to detect and manage patient complexities early within home-based care settings. This study aims to provide additional analyses regarding the impact of the EPOCA RPM system for polypathological older adult patients on the total number of unplanned hospitalization days and admissions, as well as emergency department (ED) visits. In a prior study, we evaluated the impact when the operator of the RPM system is a geriatrician. In this study, we assess the impact when the general practitioner is the operator. We used a retrospective, before-and-after cohort design. Polypathological older adult patients aged 70 and older, who benefited from the EPOCA RPM system for at least 1 year (between February 2022 and August 2024), were included in the analysis. We compared the outcomes between the previous year (Y-1) and the follow-up year (Y) by the EPOCA RPM system. Statistical analyses were significant at P value <.05. In total, 80 patients were included in the analysis, with an average age of 87. The results showed a significant reduction (P<.001) between Y-1 and Y in the total number of unplanned hospital admissions (by 57%), hospitalization days (by 49%), and ED visits (by 62%). Our findings reflected a significant decrease per patient from 0.99 to 0.42 in hospital admissions, from 0.99 to 0.37 in ED visits, and a reduction of 9.7 hospitalization days per year (P<.001). Additional analyses stratifying by hospitalization history, disability level, and caregiver status showed that the greatest effect of the RPM system was on patients with high risk and severe disability. Finally, there was no observed increase in mortality or transfers to intensive care units. Our findings are consistent with our previous results regarding the potential benefits of the EPOCA RPM system in managing care for polypathological older adult patients, this time with general practitioners as system operators. They also support existing evidence on the promise of RPM in improving care and health outcomes for older adult patients while alleviating hospital burdens by reducing unplanned hospitalizations and ED visits. It is, therefore, essential to incorporate reimbursement policies for these RPM initiatives so as to facilitate their adoption within health care systems and enhance their impact on health outcomes.

During the SARS-CoV-2 pandemic there was a considerable drop in the number of visits to Pediatric Emergency Departments (PED). Unplanned return visits (URV) might represent inadequate emergency care. We assessed the impact of the pandemic on early URV to PEDs in Israel. This multicenter cross-sectional study analyzed the 72-h URV to PEDs among patients under the age of 18 years during a one-year pandemic period (March 1st, 2020, to February 28th, 2021), and compared them with the 72-h URV of the corresponding pre-pandemic period (March 1st, 2019, to February 28th, 2020). Data was extracted from Clalit Health Services (CHS), the largest public health care organization in Israel. The pandemic and pre-pandemic early URV rates were 5465 (5.1%) and 8775 (5.6%), respectively (OR = 0.90, 95% CI 0.92–0.99). The rate of return-visit admissions to hospital wards during these periods were 29.5% and 32.1%, respectively (OR = 0.83, 95% CI 0.86–0.98). The rate of return-visit admissions to ICUs during these periods were 0.64% and 0.52%, respectively (OR = 1.11, 95% CI 0.67–1.62). On return-visit, 3 (0.055%) and 5 (0.057%) URV patients were declared dead on arrival during the pandemic and pre-pandemic periods, respectively (OR = 0.96, 95% CI 0.23–4.03). The distributions of the time interval from index visit to return visit remained consistent between the periods. In our study, early URV to PED's were only mildly influenced by the SARS-CoV-2 pandemic.

Background Providing care via telemedicine was suggested worldwide during the COVID-19 pandemic. A new care model and service flow using telephone-based telemedicine (2T SAVE-COVID project) was established to provide care for patients at the Department of Medicine during the pandemic. This study aimed to investigate the clinical outcomes of patients after receiving care through telemedicine in the project. Methods A retrospective cohort study was conducted to compare the clinical outcomes of patients receiving telemedicine compared to routine care at the outpatient clinics of the Medicine department from April 2020 to November 2021, including an original cohort (routine care: n = 54,032; telemedicine: n = 16,388) and a propensity score-matched cohort (n = 16,246 per group). Baseline and clinical characteristics, rates of unplanned visits and mortality outcomes were analyzed. Time prior to unplanned visits was calculated, and multivariate analysis was performed to identify predictors of unplanned visits. Findings In the original cohort, the telemedicine group demonstrated a significantly higher incidence of unplanned outpatient visits (19.7% vs. 18.5%, p < 0.001). Conversely, in the matched cohort, the telemedicine group showed a lower rate of unplanned outpatient visits (19.4% vs. 59.3%, p < 0.001). Multivariate analysis confirmed that telemedicine was independently associated with a reduced risk of unplanned visits (adjusted HR: 0.69, 95% CI: 0.65 – 0.74, p < 0.001). However, other predictors with an increased incidence of unplanned visits included female patients, patients with chronic kidney disease stage 3 to 5, cancer and serum albumin levels below 4 g/dL. Additionally, telemedicine is associated with significantly lower mortality rates compared to routine care. Interpretation Telephone-based telemedicine can be a viable alternative to routine care, offering comparable or improved outcomes in terms of a reduced rate of unplanned visits and lower mortality rates, particularly when combined with appropriate patient selection and monitoring strategies.

Background Accurate identification of older persons at risk of unplanned hospital visits can facilitate preventive interventions. Several risk scores have been developed to identify older adults at risk of unplanned hospital visits. It is unclear whether risk scores developed in one country, perform as well in another. This study validates seven risk scores to predict unplanned hospital admissions and emergency department (ED) visits in older home care recipients from six countries. Methods We used the IBenC sample ( n  = 2446), a cohort of older home care recipients from six countries (Belgium, Finland, Germany, Iceland, Italy and The Netherlands) to validate four specific risk scores (DIVERT, CARS, EARLI and previous acute admissions) and three frailty indicators (CHESS, Fried Frailty Criteria and Frailty Index). Outcome measures were unplanned hospital admissions, ED visits or any unplanned hospital visits after 6 months. Missing data were handled by multiple imputation. Performance was determined by assessing calibration and discrimination (area under receiver operating characteristic curve (AUC)). Results Risk score performance varied across countries. In Iceland, for any unplanned hospital visits DIVERT and CARS reached a fair predictive value (AUC 0.74 [0.68–0.80] and AUC 0.74 [0.67–0.80]), respectively). In Finland, DIVERT had fair performance predicting ED visits (AUC 0.72 [0.67–0.77]) and any unplanned hospital visits (AUC 0.73 [0.67–0.77]). In other countries, AUCs did not exceed 0.70. Conclusions Geographical validation of risk scores predicting unplanned hospital visits in home care recipients showed substantial variations of poor to fair performance across countries. Unplanned hospital visits seem considerably dependent on healthcare context. Therefore, risk scores should be validated regionally before applied to practice. Future studies should focus on identification of more discriminative predictors in order to develop more accurate risk scores.

Objective We examined the prevalence of psychological distress and its association with emergency room (ER) usage and all‐cause mortality among colorectal cancer (CRC) survivors. Methods We utilized data from the 2000–2018 National Health Interview Survey (NHIS) and the NHIS linked mortality files. The main exposure was psychological distress, assessed with the six‐item Kessler Psychological Distress Scale (K6) and classified as (no/low, moderate, severe). The outcomes were ER usage during the past 12 months and all‐cause mortality. Multivariable logistic and Cox proportional hazards models were used to examine the associations between psychological distress and ER usage and all‐cause mortality, respectively. Results A total of 3198 CRC survivors were included in the study, of whom 4.1% and 19.6% reported severe and moderate psychological distress, respectively. Approximately 30% of CRC survivors had ER use, and 41.5% of deaths occurred with a median follow‐up of 84 months. In the adjusted model, compared to CRC survivors with low/no psychological distress, those with severe (aOR = 1.83; 95% CI, 1.10–3.04) or moderate (aOR = 1.60; 95% CI, 1.21–2.10) psychological distress had higher odds of reporting ER use. However, there was no statistically significant association between psychological distress and all‐cause mortality. Conclusion CRC survivors with severe or moderate psychological distress have higher ER usage. This finding emphasizes the significance of timely identifying and addressing psychological distress to improve the quality of life and clinical outcomes of patients diagnosed with CRC. Integrating mental health support into routine cancer care may reduce distress levels, potentially leading to fewer ER usages among CRC survivors.

The primary objective of this study was to determine rates of reoperation, ED visits, and hospital readmission after thyroid and parathyroid surgery at a tertiary hospital. A secondary objective was to determine if scores from the American College of Surgeons Surgical Risk Calculator (ACS SRC) predicted these events. We retrospectively reviewed the records of patients undergoing parathyroid and thyroid surgery between 2011 and 2014. Patients who underwent an unplanned reoperation, returned to the ED, or were readmitted to hospital were evaluated using the ACS SRC. 436 patients underwent thyroid and parathyroid operations. Rates of re-operations, ED visits and hospital readmissions after thyroid and parathyroid surgery were: 3.4%, 0.6% and 3.0% and 2.2%, 0% and 1.4%, respectively. 71% of patients who experienced post-operative complications scored below average on the ACS SRC, 17% scored above average and 12% scored average risk. The SRC did not predict re-operation, ED visits, or hospital readmission after thyroid or parathyroid operations. •Readmission rate after thyroid and parathyroid surgery was similar to NSQIP.•Reoperation rate after thyroidectomy was 3.4%, most commonly due to hemorrhage.•One surgeon was responsible for over 70% of all hematoma evacuations.•Half of all emergency visits and hospital readmissions were due to hypocalcemia.•The ACS SRC didn't predict reoperations, emergency visits or readmissions.

Colonoscopy, although a low-risk procedure, is not without associated adverse events. The rates of major adverse events such as perforation and bleeding after a colonoscopy are well reported. The rates of minor incidents following a colonoscopy, however, are less well examined. Recently the Centers for Medicare and Medicaid Services (CMS) started public reporting on the quality of outpatient endoscopy facilities by using a measure of risk-standardized rates of unplanned hospital visits within 7 days of colonoscopy. We intended to record and present the characteristics of our patient population who had an unplanned hospital visit within 7 days after undergoing colonoscopy in an outpatient setting. This is a retrospective single-center observational study. During the study period of July 2018 to December 2019, we reviewed charts of all patients who returned to the emergency room within a week of undergoing an outpatient colonoscopy. Patient demographics, clinical data and details of colonoscopy were collected and analyzed. Of the 5344 outpatient colonoscopies performed, our post-colonoscopy emergency room visit rate was 1.05% (n=56). The mean age of the participants was 58 years and 55% were male; 32% of our patients reported gastrointestinal symptoms such as abdominal pain or gastrointestinal bleeding. Patients with gastrointestinal symptoms had a higher rate of polypectomies performed (36.4% vs 11.8%, P = 0.04) and reported higher illicit drug use (31.9% vs 5.9%, P = 0.02) compared with those with non-gastrointestinal complaints. After colonoscopy, 41% of the patients reported reasons for emergency room visits that were entirely unrelated to the procedure. Our study highlights that unplanned visits within 7 days of colonoscopy are not necessarily related to the procedure, and those that are, tend to be due to unavoidable patient factors. Hence the CMS measure may not be an accurate determinant of the quality of procedure or facility care delivered.

Aim This study was conducted to evaluate the impact of chemotherapy on the risk of unplanned visit in a cohort of colorectal cancer outpatients. Chief complaints for unplanned visits and risk factors for hospital admission were also analyzed. Patients and methods Clinical data of 229 consecutive colorectal cancer patients who were unexpectedly presented to our acute oncology clinic between 2006 and 2009 were reviewed. A case-crossover statistical analysis was applied to study the association between exposure to chemotherapy (trigger event) and the occurrence of unplanned visit (acute outcome) in three time windows (7, 15, and 21 days from the closest previous chemotherapy treatment). Cox model was used to assess the risk factors for hospitalization. Results There were 469 unplanned visits registered. Most of the patients had Eastern Cooperative Oncology Group performance status (ECOG PS) 0–1 (80 %) and advanced cancer stage (78 %). The majority of unplanned visits (72 %) occurred within 30 days since last chemotherapy. The most frequent presenting complaints were pain, fatigue, and anorexia. The two time windows associated with higher risk of visit were 15 and 21 days from last treatment, both for early (odds ratio [OR] 3.8, CI 1.4–10.2 and OR 3.8, CI 1.4–10.2) and advanced disease stage (OR 1.71, CI 1–2.9 and OR 3, CI 1.5–5.9). Of the unplanned visits, 10 % resulted in hospital admission. Presenting with multiple symptoms and with deteriorated PS were both predictors for hospitalization. Conclusion Chemotherapy exposition triggers the need for unplanned visits over the second and third week after treatment. The prompt and effective management of unexpected events may be cost- and time-saving and reduce pressure on oncology services.