The Potential of Evidence-Based Clinical Intake Tools to Discover or Ground Prevalence of Symptoms Using Real-Life Digital Health Encounters: Retrospective Cohort Study.

BACKGROUND: Evidence-based clinical intake tools (EBCITs) are structured assessment tools used to gather information about patients and help health care providers make informed decisions. The growing demand for personalized medicine, along with the big data revolution, has rendered EBCITs a promising solution. EBCITs have the potential to provide comprehensive and individualized assessments of symptoms, enabling accurate diagnosis, while contributing to the grounding of medical care. OBJECTIVE: This work aims to examine whether EBCITs cover data concerning disorders and symptoms to a similar extent as physicians, and thus can reliably address medical conditions in clinical settings. We also explore the potential of EBCITs to discover and ground the real prevalence of symptoms in different disorders thereby expanding medical knowledge and further supporting medical diagnoses made by physicians. METHODS: Between August 1, 2022, and January 15, 2023, patients who used the services of a digital health care (DH) provider in the United States were first assessed by the Kahun EBCIT. Kahun platform gathered and analyzed the information from the sessions. This study estimated the prevalence of patients' symptoms in medical disorders using 2 data sets. The first data set analyzed symptom prevalence, as determined by Kahun's knowledge engine. The second data set analyzed symptom prevalence, relying solely on data from the DH patients gathered by Kahun. The variance difference between these 2 prevalence data sets helped us assess Kahun's ability to incorporate new data while integrating existing knowledge. To analyze the comprehensiveness of Kahun's knowledge engine, we compared how well it covers weighted data for the symptoms and disorders found in the 2019 National Ambulatory Medical Care Survey (NMCAS). To assess Kahun's diagnosis accuracy, physicians independently diagnosed 250 of Kahun-DH's sessions. Their diagnoses were compared with Kahun's diagnoses. RESULTS: In this study, 2550 patients used Kahun to complete a full assessment. Kahun proposed 108,523 suggestions related to symptoms during the intake process. At the end of the intake process, 6496 conditions were presented to the caregiver. Kahun covered 94% (526,157,569/562,150,572) of the weighted symptoms and 91% (1,582,637,476/173,4783,244) of the weighted disorders in the 2019 NMCAS. In 90% (224/250) of the sessions, both physicians and Kahun suggested at least one identical disorder, with a 72% (367/507) total accuracy rate. Kahun's engine yielded 519 prevalences while the Kahun-DH cohort yielded 599; 156 prevalences were unique to the latter and 443 prevalences were shared by both data sets. CONCLUSIONS: ECBITs, such as Kahun, encompass extensive amounts of knowledge and could serve as a reliable database for inferring medical insights and diagnoses. Using this credible database, the potential prevalence of symptoms in different disorders was discovered or grounded. This highlights the ability of ECBITs to refine the understanding of relationships between disorders and symptoms, which further supports physicians in medical diagnosis.

Validation of a novel Artificial Pharmacology Intelligence (API) system for the management of patients with polypharmacy.

OBJECTIVE: Medication management of patients with polypharmacy is highly complex. We aimed to validate a novel Artificial Pharmacology Intelligence (API) algorithm to optimize the medication review process in a comprehensive, personalized, and scalable way. MATERIALS AND METHODS: The study was conducted on anonymized retrospective electronic health records (EHR) of 49 patients. Each patient's file was reviewed by the API system, a clinical pharmacist, and a judging committee. Validation was assessed by comparing the overall agreement of the judging committee (as the gold standard, blinded to the identity of the analyzer) to both the API system and clinical pharmacists' conclusions. Five medication-related problem (MRP) categories were assessed: duplication of therapy, age-related issues, incorrect dose, current side effects and future side effects' risk. For each category the overall validity parameters, agreement, positive predictive value (PPV), negative predictive value (NPV), sensitivity and specificity were analyzed. RESULTS: The agreement between the API system and the judging committee was 93.5 % (95 % CI 92.7-94.4), while the agreement between the clinical pharmacists and the judging committee was 73.9 % (95 % CI 72.5-75.3). The PPV was 92.2 % (90.9-93.5) and NPV was 94.2 % (93.1-95.2) for the API system and 76.3 % (69.8-82.8) and 73.5 % (72.3-74.8) respectively for the clinical pharmacists. DISCUSSION: AI systems can equip clinicians with sophisticated tools and scale manual processes such as comprehensive medication reviews, thus reducing MRPs and drug-related hospitalizations related to multidrug treatments. The API system validated in this study provided comprehensive, multidrug, multilayered analysis intended to bridge the innate complexity of personalized polypharmacy treatment. CONCLUSIONS: The API system was validated as a tool for providing actionable clinical insights non-inferior to a manual clinical review of a clinical pharmacist. The API system showed promising results in reducing MRPs.

Digitally enabled asynchronous remote medical management of anxiety and depression: A cohort study.

OBJECTIVE: To evaluate the clinical outcomes of a remote mental health program for managing anxiety and depression, primarily using asynchronous digital communication. METHODS: This retrospective cohort study examined U.S. adults seeking remote care for anxiety and depression from January 2021 to May 2022. The program involves clinician-led assessment, patient education, medication management, and ongoing monitoring, primarily via text. Anxiety and depression were measured using Patient Health Questionnaire (PHQ-9) and Generalized Anxiety Disorder (GAD-7) scores. Outcomes examined were changes in scores, 50% score improvement rate, and remission rate (score <5) at 1, 3, and 6 months. RESULTS: During the period evaluated, 11,844 program participants met the inclusion criteria. Most were female (n = 8328, 70.3%); their age ranged from 18-82 years (median 31 years). At baseline, median PHQ-9 and GAD-7 scores were 13 (IQR 9-17); 67% and 69% met score criteria for depression and anxiety, respectively. Most participants (80%) were prescribed a selective serotonin reuptake inhibitor (SSRI). By one month, average PHQ-9 and GAD-7 scores decreased significantly by 9.2 and 9.1 points (both p < .01). At 1-month follow-up, the 50% score improvement rate was 66% for PHQ-9 and 69% GAD-7 (p < .01). Scores continued to decrease with follow-up. At 3 months, over half achieved remission (percent [95% CI]: 52% [51-54] for anxiety, 53% [52-55] for depression). Similar improvement was observed at 6 months and in sensitivity analyses accounting for loss to follow-up. CONCLUSIONS: Use of a remote mental health program with digital tools was associated with significant clinical improvement in anxiety and depression. Challenges remain in maintaining patient engagement and ensuring appropriate care quality monitoring in digital mental health programs. Additional research comparing remote digital care to traditional in-person models is warranted. Studies should examine long-term outcomes, optimal care protocols, and the challenges to integrating these programs into existing healthcare systems and ensuring equitable access.

The Association of Macronutrient Consumption and BMI to Exhaled Carbon Dioxide in Lumen Users: Retrospective Real-World Study.

BACKGROUND: Metabolic flexibility is the ability of the body to rapidly switch between fuel sources based on their accessibility and metabolic requirements. High metabolic flexibility is associated with improved health outcomes and a reduced risk of several metabolic disorders. Metabolic flexibility can be improved through lifestyle changes, such as increasing physical activity and eating a balanced macronutrient diet. Lumen is a small handheld device that measures metabolic fuel usage through exhaled carbon dioxide (CO2), which allows individuals to monitor their metabolic flexibility and make lifestyle changes to enhance it. OBJECTIVE: This retrospective study aims to examine the postprandial CO2 response to meals logged by Lumen users and its relationship with macronutrient intake and BMI. METHODS: We analyzed deidentified data from 2607 Lumen users who logged their meals and measured their exhaled CO2 before and after those meals between May 1, 2023, and October 18, 2023. A linear mixed model was fitted to test the association between macronutrient consumption, BMI, age, and gender to the postprandial CO2 response, followed by a 2-way ANOVA. RESULTS: The model demonstrated significant associations (P<.001) between CO2 response after meals and both BMI and carbohydrate intake (BMI: ß=-0.112, 95% CI -0.156 to -0.069; carbohydrates: ß=0.046, 95% CI 0.034-0.058). In addition, a 2-way ANOVA revealed that higher carbohydrate intake resulted in a higher CO2 response compared to low carbohydrate intake (F2,2569=24.23; P<.001), and users with high BMI showed modest responses to meals compared with low BMI (F2,2569=5.88; P=.003). CONCLUSIONS: In this study, we show that Lumen's CO2 response is influenced both by macronutrient consumption and BMI. The results of this study highlight a distinct pattern of reduced metabolic flexibility in users with obesity, indicating the value of Lumen for assessing postprandial metabolic flexibility.

Characteristics and Symptoms of App Users Seeking COVID-19-Related Digital Health Information and Remote Services: Retrospective Cohort Study.

BACKGROUND: Patient-facing digital health tools have been promoted to help patients manage concerns related to COVID-19 and to enable remote care and self-care during the COVID-19 pandemic. It has also been suggested that these tools can help further our understanding of the clinical characteristics of this new disease. However, there is limited information on the characteristics and use patterns of these tools in practice. OBJECTIVE: The aims of this study are to describe the characteristics of people who use digital health tools to address COVID-19-related concerns; explore their self-reported symptoms and characterize the association of these symptoms with COVID-19; and characterize the recommendations provided by digital health tools. METHODS: This study used data from three digital health tools on the K Health app: a protocol-based COVID-19 self-assessment, an artificial intelligence (AI)-driven symptom checker, and communication with remote physicians. Deidentified data were extracted on the demographic and clinical characteristics of adults seeking COVID-19-related health information between April 8 and June 20, 2020. Analyses included exploring features associated with COVID-19 positivity and features associated with the choice to communicate with a remote physician. RESULTS: During the period assessed, 71,619 individuals completed the COVID-19 self-assessment, 41,425 also used the AI-driven symptom checker, and 2523 consulted with remote physicians. Individuals who used the COVID-19 self-assessment were predominantly female (51,845/71,619, 72.4%), with a mean age of 34.5 years (SD 13.9). Testing for COVID-19 was reported by 2901 users, of whom 433 (14.9%) reported testing positive. Users who tested positive for COVID-19 were more likely to have reported loss of smell or taste (relative rate [RR] 6.66, 95% CI 5.53-7.94) and other established COVID-19 symptoms as well as ocular symptoms. Users communicating with a remote physician were more likely to have been recommended by the self-assessment to undergo immediate medical evaluation due to the presence of severe symptoms (RR 1.19, 95% CI 1.02-1.32). Most consultations with remote physicians (1940/2523, 76.9%) were resolved without need for referral to an in-person visit or to the emergency department. CONCLUSIONS: Our results suggest that digital health tools can help support remote care and self-management of COVID-19 and that self-reported symptoms from digital interactions can extend our understanding of the symptoms associated with COVID-19.

"A patient like me" - An algorithm-based program to inform patients on the likely conditions people with symptoms like theirs have.

To date, consumer health tools available over the web suffer from serious limitations that lead to low quality health- related information. While health data in our world are abundant, access to it is limited because of liability and privacy constraints.The objective of the present study was to develop and evaluate an algorithm-based tool which aims at providing the public with reliable, data-driven information based and personalized information regarding their symptoms, to help them and their physicians to make better informed decisions, based on statistics describing "people like you", who have experienced similar symptoms.We studied anonymized medical records of Maccabi Health Care. The data were analyzed by employing machine learning methodology and Natural Language Processing (NLP) tools. The NLP tools were developed to extract information from unstructured free-text written by Maccabi's physicians.Using machine learning and NLP on over 670 million notes of patients' visits with Maccabi physicians accrued since 1993, we developed predictors for medical conditions based on patterns of symptoms and personal characteristics.The algorithm was launched for Maccabi insured members on January 7, 2018 and for members of Integrity Family Care program in Alabama on May 1, 2018.The App. invites the user to describe her/ his main symptom or several symptoms, and this prompts a series of questions along the path developed by the algorithm, based on the analysis of 70 million patients' visits to their physicians.Users started dialogues with 225 different types of symptoms, answering on average 22 questions before seeing how people similar to them were diagnosed. Users usually described between 3 and 4 symptoms (mean 3.2) in the health dialogue.In response to the question "conditions verified by your doctor", 82.4% of responders (895/1085) in Maccabi reported that the diagnoses suggested by K's health dialogues were in agreement with their doctor's final diagnosis. In Integrity Health Services, 85.4% of responders (111/130) were in agreement with the physicians' diagnosis.While the program achieves very high approval rates by its users, its primary achievement is the 85% accuracy in identifying the most likely diagnosis, with the gold standard being the final diagnosis made by the personal physician in each individual case. Moreover, the machine learning algorithm continues to update itself with the feedback given by users.

Ocular complications in children after hematopoietic stem cell transplantation without total body irradiation.

PURPOSE: The purpose of the study was to evaluate whether hematopoietic stem cell transplantation (HSCT) without conditioning Total Body Irradiation (TBI) had lower or milder ocular complication rates in the pediatric population. METHODS: This study included all children who underwent HSCT without conditioning TBI at the Chaim Sheba Medical Center between the years 2001 and 2008. All children had an ophthalmic evaluation prior to and every four months after HSCT. RESULTS: Of the 33 children who initially comprised this study, ten did not complete the minimal follow-up of four months, and were, thus, excluded from the study. Follow-up of the remaining 23 children ranged from four to 117 months. Dry eye related to chronic graft-versus-host disease (cGVHD) developed in eight children (35 %). In three cases, an additional complication was observed : corneal abscess, herpes zoster ophthalmicus, and bilateral subcapsular cataract (one case each). Posterior segment or neuro-ophthalmological complications were not observed in any patient. CONCLUSION: In our study group, the preclusion of conditioning TBI before HSCT did not result in a decreased ocular complication rate compared to past publications, but complications were relatively mild and confined only to the anterior segment.