BACKGROUND: Early post-discharge assessments for newborns are recommended. Virtual care has become more prevalent during the pandemic, providing an opportunity to better understand its impact on the quality of post-discharge newborn care. The objective of this study was to understand whether primary care visit modality (in-person vs. virtual) is associated with early newborn hospital readmissions and emergency department (ED) visits.
METHODS: We conducted a population-based, case-control study using linked health administrative databases between September 1, 2020 and March 31, 2022 in Ontario, Canada. We compared the modality of primary care visits among cases (hospital readmission within 14 days of life) and controls (newborns without a readmission), matched on infant sex, gestational age, and maternal parity. We included an alternative definition of cases as a composite of either a newborn hospital readmission or emergency department (ED) visit or in-hospital death within the first 14 days of life. Conditional logistic regression models were used to model odds ratios (ORs), comparing those exposed to a virtual visit versus in-person visit, adjusting for infant birth weight, birth hospitalization length of stay, neighbourhood level material deprivation, rurality and presence of active maternal comorbidities.
RESULTS: Among 73,324 eligible newborns, 2,220 experienced a hospital readmission within 14 days of life and were matched to 8,880 controls. Jaundice was the primary reason for readmission (75% of readmissions). Compared to newborns who were seen in-person post-discharge, newborns who were seen virtually had higher odds of hospital readmission (adjusted odds ratio [aOR] 1.41 (95% CI 1.09, 1.83); the magnitude of effect was not different using the composite outcome (aOR 1.35, 95% CI 1.05, 1.75).
CONCLUSIONS: Newborns who receive a virtual post-discharge visit are more likely than those who receive an in-person visit to require hospital readmission.

Hypertension (HTN) is the dominant cause of cardiovascular disease and premature death worldwide. Also in Poland, the number of people with HTN is steadily increasing. In order to improve care for patients with HTN and other chronic diseases, a pilot of the POZ PLUS coordinated-care model was introduced. The pilot ran from 1 July 2018 to 30 September 2021 at 47 facilities nationwide. The purpose of this study was to conduct a preliminary analysis of the effectiveness of this model of care. The study focused on the management of pharmacotherapy in patients with hypertension and other comorbidities. The study included a group of 90 patients with HTN. Fifty-nine people were in the coordinated-care study group and 31 in the control group. Data were collected from electronic medical records. The analysis showed a trend toward greater blood-pressure reduction in patients under coordinated care (-4 mmHg difference in systolic blood pressure between the second and first visits and -2 mmHg difference in diastolic pressure between the second and first visits, p = 0.180 and p = 0.156). This suggests the preliminary conclusion that coordinated care in the PCP plus model might have positively affected the outcomes of patients with HTN. Further studies on the subject are planned.

BACKGROUND: Identification and referral of at-risk patients from primary care practitioners (PCPs) to eye care professionals remain a challenge. Approximately 1.9 million Americans suffer from vision loss as a result of undiagnosed or untreated ophthalmic conditions. In ophthalmology, artificial intelligence (AI) is used to predict glaucoma progression, recognize diabetic retinopathy (DR), and classify ocular tumors; however, AI has not yet been used to triage primary care patients for ophthalmology referral.
OBJECTIVE: This study aimed to build and compare machine learning (ML) methods, applicable to electronic health records (EHRs) of PCPs, capable of triaging patients for referral to eye care specialists.
METHODS: Accessing the Optum deidentified EHR data set, 743,039 patients with 5 leading vision conditions (age-related macular degeneration [AMD], visually significant cataract, DR, glaucoma, or ocular surface disease [OSD]) were exact-matched on age and gender to 743,039 controls without eye conditions. Between 142 and 182 non-ophthalmic parameters per patient were input into 5 ML methods: generalized linear model, L1-regularized logistic regression, random forest, Extreme Gradient Boosting (XGBoost), and J48 decision tree. Model performance was compared for each pathology to select the most predictive algorithm. The area under the curve (AUC) was assessed for all algorithms for each outcome.
RESULTS: XGBoost demonstrated the best performance, showing, respectively, a prediction accuracy and an AUC of 78.6% (95% CI 78.3%-78.9%) and 0.878 for visually significant cataract, 77.4% (95% CI 76.7%-78.1%) and 0.858 for exudative AMD, 79.2% (95% CI 78.8%-79.6%) and 0.879 for nonexudative AMD, 72.2% (95% CI 69.9%-74.5%) and 0.803 for OSD requiring medication, 70.8% (95% CI 70.5%-71.1%) and 0.785 for glaucoma, 85.0% (95% CI 84.2%-85.8%) and 0.924 for type 1 nonproliferative diabetic retinopathy (NPDR), 82.2% (95% CI 80.4%-84.0%) and 0.911 for type 1 proliferative diabetic retinopathy (PDR), 81.3% (95% CI 81.0%-81.6%) and 0.891 for type 2 NPDR, and 82.1% (95% CI 81.3%-82.9%) and 0.900 for type 2 PDR.
CONCLUSIONS: The 5 ML methods deployed were able to successfully identify patients with elevated odds ratios (ORs), thus capable of patient triage, for ocular pathology ranging from 2.4 (95% CI 2.4-2.5) for glaucoma to 5.7 (95% CI 5.0-6.4) for type 1 NPDR, with an average OR of 3.9. The application of these models could enable PCPs to better identify and triage patients at risk for treatable ophthalmic pathology. Early identification of patients with unrecognized sight-threatening conditions may lead to earlier treatment and a reduced economic burden. More importantly, such triage may improve patients\' lives.

BACKGROUND: The COVID-19 pandemic has led to the rapid proliferation of artificial intelligence (AI), which was not previously anticipated; this is an unforeseen development. The use of AI in health care settings is increasing, as it proves to be a promising tool for transforming health care systems, improving operational and business processes, and efficiently simplifying health care tasks for family physicians and health care administrators. Therefore, it is necessary to assess the perspective of family physicians on AI and its impact on their job roles.
OBJECTIVE: This study aims to determine the impact of AI on the management and practices of Qatar\'s Primary Health Care Corporation (PHCC) in improving health care tasks and service delivery. Furthermore, it seeks to evaluate the impact of AI on family physicians\' job roles, including associated risks and ethical ramifications from their perspective.
METHODS: We conducted a cross-sectional survey and sent a web-based questionnaire survey link to 724 practicing family physicians at the PHCC. In total, we received 102 eligible responses.
RESULTS: Of the 102 respondents, 72 (70.6%) were men and 94 (92.2%) were aged between 35 and 54 years. In addition, 58 (56.9%) of the 102 respondents were consultants. The overall awareness of AI was 80 (78.4%) out of 102, with no difference between gender (P=.06) and age groups (P=.12). AI is perceived to play a positive role in improving health care practices at PHCC (P<.001), managing health care tasks (P<.001), and positively impacting health care service delivery (P<.001). Family physicians also perceived that their clinical, administrative, and opportunistic health care management roles were positively influenced by AI (P<.001). Furthermore, perceptions of family physicians indicate that AI improves operational and human resource management (P<.001), does not undermine patient-physician relationships (P<.001), and is not considered superior to human physicians in the clinical judgment process (P<.001). However, its inclusion is believed to decrease patient satisfaction (P<.001). AI decision-making and accountability were recognized as ethical risks, along with data protection and confidentiality. The optimism regarding using AI for future medical decisions was low among family physicians.
CONCLUSIONS: This study indicated a positive perception among family physicians regarding AI integration into primary care settings. AI demonstrates significant potential for enhancing health care task management and overall service delivery at the PHCC. It augments family physicians\' roles without replacing them and proves beneficial for operational efficiency, human resource management, and public health during pandemics. While the implementation of AI is anticipated to bring benefits, the careful consideration of ethical, privacy, confidentiality, and patient-centric concerns is essential. These insights provide valuable guidance for the strategic integration of AI into health care systems, with a focus on maintaining high-quality patient care and addressing the multifaceted challenges that arise during this transformative process.

Beta-thalassemia (β-thalassemia) is a hematologic genetic condition that causes microcytic anemia due to defective synthesis of the hemoglobin beta chain. As a hypochromic microcytic anemia that is commonly associated with symptoms such as fatigue and pallor when identified in adulthood, β-thalassemia may be commonly underdiagnosed or misdiagnosed as iron deficiency anemia. This study presents a case of a patient with β-thalassemia who was initially misdiagnosed with treatment-resistant iron deficiency anemia. Here, we present the case of a 66-year-old male of Mediterranean descent with a history of military service who presented with persistent fatigue. He had a past medical history of hypertension, diabetes mellitus type 2, sleep apnea, and iron deficiency anemia. Despite undergoing unnecessarily prolonged iron supplementation for suspected iron deficiency anemia, the patient\'s complete blood count and peripheral blood smear continued to identify hypochromic microcytic anemia. Ultimately, hemoglobin electrophoresis was performed, and mutations were identified in the hemoglobin beta chain consistent with β-thalassemia minor. Due to its rarity and wide variation in presentation, β-thalassemia may be frequently misdiagnosed. β-thalassemia is a spectrum of disorders ranging from β-thalassemia minor, which may be asymptomatic and incidentally discovered in adulthood, to β-thalassemia major, which may include bone marrow deformities from extramedullary hematopoiesis and require frequent blood transfusions to sustain life. Therefore, patients who present with symptoms of β-thalassemia minor may not be identified until later in life after undergoing decades of ineffective treatment. β-thalassemia is a multifactorial disease with a variety of clinical presentations that can easily be misdiagnosed as other types of anemia. This case highlights the importance of performing thorough laboratory testing and casting a wide net of differential diagnoses when evaluating patients with treatment-resistant anemia. This case calls for further research on the genetic contributions to β-thalassemia as well as improved ways to identify this disorder, particularly in patients who may not have a severe form that is easily diagnosed in early childhood.

An artificial intelligence-based case-finding strategy has been developed to systematically identify individuals with osteoporosis or at varying risk of fragility fracture. This strategy has the potential to close the critical care gap in osteoporosis treatment in primary care, thereby lessening the societal burden imposed by fragility fractures.
BACKGROUND: Osteoporotic fractures represent a major cause of morbidity and, in older adults, a precursor of disability, loss of independence, poor quality of life and premature death. Despite the detrimental health impact, osteoporosis remains largely underdiagnosed and undertreated worldwide. Subjects at risk for osteoporosis-related fractures are identified either via organised screening or case finding. In the absence of a population-based screening policy, subjects at high risk of fragility fractures are opportunistically identified when a fracture occurs or because of other clinical risk factors (CRFs) for osteoporotic fracture and areal bone mineral density (aBMD) measured by dual-energy X-ray absorptiometry (DXA).
OBJECTIVE: This paper describes the development of a novel case-finding strategy, named Osteoporosis Diagnostic and Therapeutic Pathway (ODTP), which enables to identify subjects with osteoporosis or at varying risk of fragility fracture. This strategy is based on a specifically designed software tool, named \"Bone Fragility Query\" (BFQ), which analyses the electronic health record (EHR) databases of General Practitioners (GPs) to systematically identify individuals who should be prescribed DXA-BMD measurement, vertebral fracture assessment (VFA) and anti-osteoporosis medications (AOM).
CONCLUSIONS: The ODTP through BFQ tool is a feasible, convenient and time-saving osteoporosis model of care for GPs during routine clinical practice. It enables GPs to shift their focus from what to do (clinical guidelines) to how to do it in the primary health care setting. It also allows a systematic approach to primary and secondary prevention of fragility fractures, thereby overcoming clinical inertia and contributing to closing the gap between evidence and practice for the management of osteoporosis in primary care.

UNASSIGNED: Factors associated with the appropriateness of antibiotic prescribing in primary care have been poorly explored. In particular, the impact of computerised decision-support systems (CDSS) remains unknown.
UNASSIGNED: We aim at investigating the uptake of CDSS and its association with physician characteristics and professional activity.
UNASSIGNED: Since May 2022, users of a CDSS for antibiotic prescribing in primary care in France have been invited, when registering, to complete three case vignettes assessing clinical situations frequently encountered in general practice and identified as at risk of antibiotic misuse. Appropriateness of antibiotic prescribing was defined as the rate of answers in line with the current guidelines, computed by individuals and by specific questions. Physician\'s characteristics associated with individual appropriate antibiotic prescribing (< 50%, 50-75% and > 75% appropriateness) were identified by multivariate ordinal logistic regression.
UNASSIGNED: In June 2023, 60,067 physicians had registered on the CDSS. Among the 13,851 physicians who answered all case vignettes, the median individual appropriateness level of antibiotic prescribing was 77.8% [Interquartile range, 66.7%-88.9%], and was < 50% for 1,353 physicians (10%). In the multivariate analysis, physicians\' characteristics associated with appropriateness were prior use of the CDSS (OR = 1.71, 95% CI 1.56-1.87), being a general practitioner vs. other specialist (OR = 1.34, 95% CI 1.20-1.49), working in primary care (OR = 1.14, 95% CI 1.02-1.27), mentoring students (OR = 1.12, 95% CI 1.04-1.21) age (OR = 0.69 per 10 years increase, 95% CI 0.67-0.71).
UNASSIGNED: Individual appropriateness for antibiotic prescribing was high among CDSS users, with a higher rate in young general practitioners, previously using the system. CDSS could improve antibiotic prescribing in primary care.
Individual appropriateness for antibiotic prescribing is high among CDSS users.CDSS use could passively improve antibiotic prescribing in primary care.Factors associated with appropriateness for antibiotic prescribing for primary care diseases are: prior use of CDSS, general practice speciality vs. other specialities, younger age and mentoring of students.

BACKGROUND: Common interventions for musculoskeletal pain disorders either lack evidence to support their use or have small to modest or short-term effects. Given the heterogeneity of patients with musculoskeletal pain disorders, treatment guidelines and systematic reviews have limited transferability to clinical practice. A problem-solving method in artificial intelligence, case-based reasoning (CBR), where new problems are solved based on experiences from past similar problems, might offer guidance in such situations.
OBJECTIVE: This study aims to use CBR to build a decision support system for patients with musculoskeletal pain disorders seeking physiotherapy care. This study describes the development of the CBR system SupportPrim PT and demonstrates its ability to identify similar patients.
METHODS: Data from physiotherapy patients in primary care in Norway were collected to build a case base for SupportPrim PT. We used the local-global principle in CBR to identify similar patients. The global similarity measures are attributes used to identify similar patients and consisted of prognostic attributes. They were weighted in terms of prognostic importance and choice of treatment, where the weighting represents the relevance of the different attributes. For the local similarity measures, the degree of similarity within each attribute was based on minimal clinically important differences and expert knowledge. The SupportPrim PT\'s ability to identify similar patients was assessed by comparing the similarity scores of all patients in the case base with the scores on an established screening tool (the short form Örebro Musculoskeletal Pain Screening Questionnaire [ÖMSPQ]) and an outcome measure (the Musculoskeletal Health Questionnaire [MSK-HQ]) used in musculoskeletal pain. We also assessed the same in a more extensive case base.
RESULTS: The original case base contained 105 patients with musculoskeletal pain (mean age 46, SD 15 years; 77/105, 73.3% women). The SupportPrim PT consisted of 29 weighted attributes with local similarities. When comparing the similarity scores for all patients in the case base, one at a time, with the ÖMSPQ and MSK-HQ, the most similar patients had a mean absolute difference from the query patient of 9.3 (95% CI 8.0-10.6) points on the ÖMSPQ and a mean absolute difference of 5.6 (95% CI 4.6-6.6) points on the MSK-HQ. For both ÖMSPQ and MSK-HQ, the absolute score difference increased as the rank of most similar patients decreased. Patients retrieved from a more extensive case base (N=486) had a higher mean similarity score and were slightly more similar to the query patients in ÖMSPQ and MSK-HQ compared with the original smaller case base.
CONCLUSIONS: This study describes the development of a CBR system, SupportPrim PT, for musculoskeletal pain in primary care. The SupportPrim PT identified similar patients according to an established screening tool and an outcome measure for patients with musculoskeletal pain.

BACKGROUND: People under the care of mental health services are at increased risk of suicide. Existing studies are small in scale and lack comparisons.
OBJECTIVE: To identify opportunities for suicide prevention and underpinning data enhancement in people with recent contact with mental health services.
METHODS: This population-based study includes people who died by suicide in the year following a mental health services contact in Wales, 2001-2015 (cases), paired with similar patients who did not die by suicide (controls). We linked the National Confidential Inquiry into Suicide and Safety in Mental Health and the Suicide Information Database - Cymru with primary and secondary healthcare records. We present results of conditional logistic regression.
RESULTS: We matched 1031 cases with 5155 controls. In the year before their death, 98.3% of cases were in contact with healthcare services, and 28.5% presented with self-harm. Cases had more emergency department contacts (odds ratio 2.4, 95% CI 2.1-2.7) and emergency hospital admissions (odds ratio 1.5, 95% CI 1.4-1.7), but fewer primary care contacts (odds ratio 0.7, 95% CI 0.6-0.9) and out-patient appointments (odds ratio 0.2, 95% CI 0.2-0.3) than controls. Odds ratios were larger in females than males for injury and poisoning (odds ratio: 3.3 (95% CI 2.5-4.5) v. 2.6 (95% CI 2.1-3.1)).
CONCLUSIONS: We may be missing existing opportunities to intervene, particularly in emergency departments and hospital admissions with self-harm presentations and with unattributed self-harm, especially in females. Prevention efforts should focus on strengthening routine care contacts, responding to emergency contacts and better self-harm care. There are benefits to enhancing clinical audit systems with routinely collected data.

UNASSIGNED: Vascular anomalies are a spectrum of disorders, including vascular tumors and malformations, that often require multispecialty care. The rarity and variety of these lesions make diagnosis, treatment, and management challenging. Despite the recognition of the medical complexity and morbidity associated with vascular anomalies, there is a general lack of education on the subject for pediatric primary care and subspecialty providers. A needs assessment and the lack of an available standardized teaching tool presented an opportunity to create an educational workshop for pediatric trainees using the POGIL (process-oriented guided inquiry learning) framework.
UNASSIGNED: We developed a 2-hour workshop consisting of an introductory didactic followed by small- and large-group collaboration and case-based discussion. The resource included customizable content for learning assessment and evaluation. Residents completed pre- and posttest assessments of content and provided written evaluations of the teaching session.
UNASSIGNED: Thirty-four learners in pediatrics participated in the workshop. Session evaluations were positive, with Likert responses of 4.6-4.8 out of 5 on all items. Pre- and posttest comparisons of four content questions showed no overall statistically significant changes in correct response rates. Learners indicated plans to use the clinical content in their practice and particularly appreciated the interactive teaching forum and the comprehensive overview of vascular anomalies.
UNASSIGNED: Vascular anomalies are complex, potentially morbid, and often lifelong conditions; multispecialty collaboration is key to providing comprehensive care for affected patients. This customizable resource offers a framework for trainees in pediatrics to appropriately recognize, evaluate, and refer patients with vascular anomalies.