Prolonged, inappropriate hospital stay after patients’ eligibility for discharge from internal medicine departments is a world-wide health-care systems’ problem. Nevertheless, the extent to which such surplus hospital stays are associated with infectious complications, their time frame of appearance and their long-term implications was not previously addressed. We conducted a retrospective cohort analysis of patients experiencing an In-hospital Waiting Period (IHWP) after discharge eligibility in a single, tertiary hospital. We screened the records of 245 patients out of which 104 patients fulfilled our inclusion criteria. The mean length of IHWP was 15.7 ± 4.79 day during which 9(8.7 %) patients died. The study primary composite end-point, in-hospital mortality or hospital acquired infection (pneumonia, UTI or sepsis) occurred in 32(31 %) patients. The most hazardous time was during the first 3 IHWP days: 63.7 % of patients experienced a complication and 44 % of the total complications occurred during this period. The occurrence of any complication during IHWP was associated, with statistical significance, with increased risk of mortality during the first year after IHWP initiation (HR = 6.02, p = 0.014). Prolongation of hospital stay after patients are deemed to be discharged from internal medicine departments is associated with increased morbidity and mortality, mainly during the first surplus days of in-hospital stay. Efforts should be made to shorten such hospital stays as much as possible.

Background: Drug prescription errors are made, worldwide, on a daily basis, resulting in a high burden of morbidity and mortality. Existing rule-based systems for prevention of such errors are unsuccessful and associated with substantial burden of false alerts. Objective: In this prospective study, we evaluated the accuracy, validity, and clinical usefulness of medication error alerts generated by a novel system using outlier detection screening algorithms, used on top of a legacy standard system, in a real-life inpatient setting. Materials and Methods: We integrated a novel outlier system into an existing electronic medical record system, in a single medical ward in a tertiary medical center. The system monitored all drug prescriptions written during 16 months. The department’s staff assessed all alerts for accuracy, clinical validity, and usefulness. We recorded all physician’s real-time responses to alerts generated. Results: The alert burden generated by the system was low, with alerts generated for 0.4% of all medication orders. Sixty percent of the alerts were flagged after the medication was already dispensed following changes in patients’ status which necessitated medication changes (eg, changes in vital signs). Eighty-five percent of the alerts were confirmed clinically valid, and 80% were considered clinically useful. Forty-three percent of the alerts caused changes in subsequent medical orders. Conclusion: A clinical decision support system that used a probabilistic, machine-learning approach based on statistically derived outliers to detect medication errors generated clinically useful alerts. The system had high accuracy, low alert burden and low false-positive rate, and led to changes in subsequent orders.

Background: The task of writing multiple choice question examinations for medical students is complex, timely and requires significant efforts from clinical staff and faculty. Applying artificial intelligence algorithms in this field of medical education may be advisable. Methods: During March to April 2023, we utilized GPT-4, an OpenAI application, to write a 210 multi choice questions-MCQs examination based on an existing exam template and thoroughly investigated the output by specialist physicians who were blinded to the source of the questions. Algorithm mistakes and inaccuracies, as identified by specialists were classified as stemming from age, gender or geographical insensitivities. Results: After inputting a detailed prompt, GPT-4 produced the test rapidly and effectively. Only 1 question (0.5%) was defined as false; 15% of questions necessitated revisions. Errors in the AI-generated questions included: the use of outdated or inaccurate terminology, age-sensitive inaccuracies, gender-sensitive inaccuracies, and geographically sensitive inaccuracies. Questions that were disqualified due to flawed methodology basis included elimination-based questions and questions that did not include elements of integrating knowledge with clinical reasoning. Conclusion: GPT-4 can be used as an adjunctive tool in creating multi-choice question medical examinations yet rigorous inspection by specialist physicians remains pivotal.

Background: Non-small cell lung cancer (NSCLC) is a common and highly lethal disease. As advanced treatment modalities are being developed, improved prognostication methods are sought. L3 skeletal muscle index (L3SMI) and alanine aminotransferase (ALT) levels are accepted surrogate markers of sarcopenia and related frailty. We aimed to evaluate the potential association of these markers with NSCLC patients’ survival. Methods: A retrospective, single-center study of an NSCLC patients’ cohort. L3SMI was calculated based on skeletal muscle area on computed tomography scans at the level of the L3 vertebra. Clinical data were extracted from clinical charts. Results: A total of 140 patients (56.4% males, median age 66 [range 37–86]) were included in this study, 32% were diagnosed at stage 3 and 45% at stage 4. During the follow-up duration (median of 1.9 years; range 1 month to 6.4 years), 102 patients (72.8%) died. Patients’ characteristics that were found to be associated with increased mortality were performance status, albumin and tumor stage at diagnosis. Sarcopenia, defined as low L3SMI (lower than 41 cm^2,/m^2, for women and lower than 53 cm^2,/m^2, for men) was significantly associated with higher risk of mortality compared with patients with normal L3SMI values (77.2%, vs 64.6%, p=0.013) in univariate analysis, but not in a multiple regression analysis. Conclusion: Low L3SMI could serve as a surrogate marker for sarcopenia and frailty and, as such, facilitate the prognostication process of NSCLC patients.

Background: Low blood ALT, Alanine aminotransferase activity and high FRAIL (Fatigue, Resistance, Ambulation, Illnesses and Loss of Weight) questionnaire scores were previously shown to be associated with frailty and increased risk of mortality. We aimed to correlate these tools with mortality and each other in patients hospitalized in an internal medicine department. Methods: This is a prospective study in a large tertiary hospital. We assessed the predictive value for clinical outcomes of both low ALT blood activity and the pre-frail and frail categories of the “FRAIL” questionnaire. Results: During a 15 months study, 179 consecutive patients were recruited, of whom 20 died. When all study participants were divided to three groups according to admission ALT levels (below 10 IU/L, 11 to 19 IU/L and above 20 IU/L) we found a statistically significant difference in the rate of mortality: 4 patients died within the group of ALT < 10 IU/L, 14 patients died in the group of 10 IU/L < ALT < 19 IU/L and in the group of patients with ALT > 20 IU/L, only 2 patients died (p = 0.042). A higher score on the FRAIL questionnaire was associated, with statistical significance, with higher risk of mortality (p = 0.029). There was a significant correlation (p = 0.038) between blood ALT activity and the pre-frailty and frailty classifications by the FRAIL Questionnaire. Conclusions: Both the FRAIL questionnaire and blood ALT activity are simple and practical tools for frailty assessment and risk stratification of patients hospitalized in the internal medicine department. Both tool’s results also correlate with each other.

Low alanine aminotransferase (ALT) blood levels are known to be associated with frailty and increased risk of long-term mortality in certain populations. However, the contribution of this marker to long-term outcome has not been assessed in patients with chronic coronary heart disease.