Investigating the impact of age and sex on cataract surgery complications and outcomes.

Cataract surgery, a common procedure for vision restoration, exhibits variable outcomes based on patient demographics. This study aimed to elucidate the effects of age and sex on risk factors, intraoperative complications, and postoperative outcomes of cataract surgery. A single-center retrospective cohort study analyzed 691 eyes from 589 individuals who underwent surgery at a tertiary referral center, using electronic medical records to assess preoperative risk factors, intraoperative complications, and best corrected visual acuity (BCVA) pre- and post-operatively, alongside demographic data.

Using Formal Knowledge to Support Episodic Evidence-Based Nursing Care.

Applying evidence-based medicine prevents medical errors highlighting the need for applying Clinical Guidelines (CGs) to improve patient care by nurses. However, nurses often face challenges in utilizing CGs due to patient-specific needs. Developing a Clinical Decision Support System (CDSS) can provide real-time context-sensitive CG-based recommendations.

Supporting the Episodic Application of Clinical Guidelines over Significant Time Periods.

Medical errors contribute significantly to morbidity and mortality, emphasizing the critical role of Clinical Guidelines (GLs) in patient care. Automating GL application can enhance GL adherence, improve patient outcomes, and reduce costs. However, several barriers exist to GL implementation and real-time automated support.

Prediction of proliferative diabetic retinopathy using machine learning in Latino and non-Hispanic black cohorts with routine blood and urine testing.

Purpose: The objective was to predict proliferative diabetic retinopathy (PDR) in non-Hispanic Black (NHB) and Latino (LA) patients by applying machine learning algorithms to routinely collected blood and urine laboratory results.

Methods: Electronic medical records of 1124 type 2 diabetes patients treated at the Bronxcare Hospital eye clinic between January and December 2019 were analysed. Data collected included demographic information (ethnicity, age and sex), blood (fasting glucose, haemoglobin A1C [HbA1c] high-density lipoprotein [HDL], low-density lipoprotein [LDL], serum creatinine and estimated glomerular filtration rate [eGFR]) and urine (albumin-to-creatinine ratio [ACR]) test results and the outcome measure of retinopathy status.

Implementation and evaluation of a system for assessment of the quality of long-term management of patients at a geriatric hospital.

Background: The increasing aging population presents a significant challenge, accompanied by a shortage of professional caregivers, adding to the therapeutic burden. Clinical decision support systems, utilizing computerized clinical guidelines, can improve healthcare quality, reduce expenses, save time, and boost caregiver efficiency.

Objectives: 1) Develop and evaluate an automated quality assessment (QA) system for retrospective longitudinal care quality analysis, focusing on clinical staff adherence to evidence-based guidelines (GLs).

Ophthalmology Residents' Experience in Cataract Surgery: Preoperative Risk Factors, Intraoperative Complications, and Surgical Outcomes.

Introduction: This retrospective study explores the connection between preoperative patient risk factors, the experience of ophthalmology residents, and the outcomes of cataract surgeries performed at Hadassah Medical Center. It is hypothesized that with increased experience, residents may demonstrate greater proficiency in handling surgeries on higher-risk patients, potentially leading to improved surgical outcomes overall.

Methods: Data were examined from 691 consecutive cataract surgeries in 590 patients, conducted by ophthalmology residents at Hadassah Medical Center (January 2018 to February 2022).

Multi-Dimensional Validation of the Integration of Syntactic and Semantic Distance Measures for Clustering Fibromyalgia Patients in the Rheumatic Monitor Big Data Study.

This study primarily aimed at developing a novel multi-dimensional methodology to discover and validate the optimal number of clusters. The secondary objective was to deploy it for the task of clustering fibromyalgia patients. We present a comprehensive methodology that includes the use of several different clustering algorithms, quality assessment using several syntactic distance measures (the Silhouette Index (SI), Calinski-Harabasz index (CHI), and Davies-Bouldin index (DBI)), stability assessment using the adjusted Rand index (ARI), and the validation of the internal semantic consistency of each clustering option via the performance of multiple clustering iterations after the repeated bagging of the data to select multiple partial data sets.

Objective quantification of viewing behaviours during printed and electronic tasks in emmetropic and myopic ultra-Orthodox Jewish men.

Purpose: Ultra-Orthodox Jewish men are known to have a high prevalence of myopia, which may be due to intense near-work from an early age. This study objectively assessed near-viewing behaviours in ultra-Orthodox and non-ultra-Orthodox men in Israel for different tasks.

Methods: Ultra-Orthodox (n = 30) and non-ultra-Orthodox (n = 38) men aged 18-33 years participated.

Distributed application of guideline-based decision support through mobile devices: Implementation and evaluation.

Background: Traditionally guideline (GL)-based Decision Support Systems (DSSs) use a centralized infrastructure to generate recommendations to care providers, rather than to patients at home. However, managing patients at home is often preferable, reducing costs and empowering patients. Thus, we wanted to explore an option in which patients, in particular chronic patients, might be assisted by a local DSS, which interacts as needed with the central DSS engine, to manage their disease outside the standard clinical settings.

Evaluation of an automated knowledge-based textual summarization system for longitudinal clinical data, in the intensive care domain.

Objectives: To examine the feasibility of the automated creation of meaningful free-text summaries of longitudinal clinical records, using a new general methodology that we had recently developed; and to assess the potential benefits to the clinical decision-making process of using such a method to generate draft letters that can be further manually enhanced by clinicians.

Methods: We had previously developed a system, CliniText (CTXT), for automated summarization in free text of longitudinal medical records, using a clinical knowledge base. In the current study, we created an Intensive Care Unit (ICU) clinical knowledge base, assisted by two ICU clinical experts in an academic tertiary hospital.

Assessment of a personalized and distributed patient guidance system.

Objectives: The MobiGuide project aimed to establish a ubiquitous, user-friendly, patient-centered mobile decision-support system for patients and for their care providers, based on the continuous application of clinical guidelines and on semantically integrated electronic health records. Patients would be empowered by the system, which would enable them to lead their normal daily lives in their regular environment, while feeling safe, because their health state would be continuously monitored using mobile sensors and self-reporting of symptoms. When conditions occur that require medical attention, patients would be notified as to what they need to do, based on evidence-based guidelines, while their medical team would be informed appropriately, in parallel.

An automated knowledge-based textual summarization system for longitudinal, multivariate clinical data.

Objectives: Design and implement an intelligent free-text summarization system: The system's input includes large numbers of longitudinal, multivariate, numeric and symbolic clinical raw data, collected over varying periods of time, and in different complex contexts, and a suitable medical knowledge base. The system then automatically generates a textual summary of the data. We aim to prove the feasibility of implementing such a system, and to demonstrate its potential benefits for clinicians and for enhancement of quality of care.

Generation of Natural-Language Textual Summaries from Longitudinal Clinical Records.

Physicians are required to interpret, abstract and present in free-text large amounts of clinical data in their daily tasks. This is especially true for chronic-disease domains, but holds also in other clinical domains. We have recently developed a prototype system, CliniText, which, given a time-oriented clinical database, and appropriate formal abstraction and summarization knowledge, combines the computational mechanisms of knowledge-based temporal data abstraction, textual summarization, abduction, and natural-language generation techniques, to generate an intelligent textual summary of longitudinal clinical data.