PresRecST: a novel herbal prescription recommendation algorithm for real-world patients with integration of syndrome differentiation and treatment planning.

OBJECTIVES: Herbal prescription recommendation (HPR) is a hot topic and challenging issue in field of clinical decision support of traditional Chinese medicine (TCM). However, almost all previous HPR methods have not adhered to the clinical principles of syndrome differentiation and treatment planning of TCM, which has resulted in suboptimal performance and difficulties in application to real-world clinical scenarios. MATERIALS AND METHODS: We emphasize the synergy among diagnosis and treatment procedure in real-world TCM clinical settings to propose the PresRecST model, which effectively combines the key components of symptom collection, syndrome differentiation, treatment method determination, and herb recommendation. This model integrates a self-curated TCM knowledge graph to learn the high-quality representations of TCM biomedical entities and performs 3 stages of clinical predictions to meet the principle of systematic sequential procedure of TCM decision making. RESULTS: To address the limitations of previous datasets, we constructed the TCM-Lung dataset, which is suitable for the simultaneous training of the syndrome differentiation, treatment method determination, and herb recommendation. Overall experimental results on 2 datasets demonstrate that the proposed PresRecST outperforms the state-of-the-art algorithm by significant improvements (eg, improvements of P@5 by 4.70%, P@10 by 5.37%, P@20 by 3.08% compared with the best baseline). DISCUSSION: The workflow of PresRecST effectively integrates the embedding vectors of the knowledge graph for progressive recommendation tasks, and it closely aligns with the actual diagnostic and treatment procedures followed by TCM doctors. A series of ablation experiments and case study show the availability and interpretability of PresRecST, indicating the proposed PresRecST can be beneficial for assisting the diagnosis and treatment in real-world TCM clinical settings. CONCLUSION: Our technology can be applied in a progressive recommendation scenario, providing recommendations for related items in a progressive manner, which can assist in providing more reliable diagnoses and herbal therapies for TCM clinical task.

Developing an integrated clinical decision support system for the early identification and management of kidney disease-building cross-sectoral partnerships.

BACKGROUND: The burden of chronic conditions is growing in Australia with people in remote areas experiencing high rates of disease, especially kidney disease. Health care in remote areas of the Northern Territory (NT) is complicated by a mobile population, high staff turnover, poor communication between health services and complex comorbid health conditions requiring multidisciplinary care. AIM: This paper aims to describe the collaborative process between research, government and non-government health services to develop an integrated clinical decision support system to improve patient care. METHODS: Building on established partnerships in the government and Aboriginal Community-Controlled Health Service (ACCHS) sectors, we developed a novel digital clinical decision support system for people at risk of developing kidney disease (due to hypertension, diabetes, cardiovascular disease) or with kidney disease. A cross-organisational and multidisciplinary Steering Committee has overseen the design, development and implementation stages. Further, the system's design and functionality were strongly informed by experts (Clinical Reference Group and Technical Working Group), health service providers, and end-user feedback through a formative evaluation. RESULTS: We established data sharing agreements with 11 ACCHS to link patient level data with 56 government primary health services and six hospitals. Electronic Health Record (EHR) data, based on agreed criteria, is automatically and securely transferred from 15 existing EHR platforms. Through clinician-determined algorithms, the system assists clinicians to diagnose, monitor and provide guideline-based care for individuals, as well as service-level risk stratification and alerts for clinically significant events. CONCLUSION: Disconnected health services and separate EHRs result in information gaps and a health and safety risk, particularly for patients who access multiple health services. However, barriers to clinical data sharing between health services still exist. In this first phase, we report how robust partnerships and effective governance processes can overcome these barriers to support clinical decision making and contribute to holistic care.

A qualitative analysis of algorithm-based decision support usability testing for symptom management across the trajectory of cancer care: one size does not fit all.

BACKGROUND: Adults with cancer experience symptoms that change across the disease trajectory. Due to the distress and cost associated with uncontrolled symptoms, improving symptom management is an important component of quality cancer care. Clinical decision support (CDS) is a promising strategy to integrate clinical practice guideline (CPG)-based symptom management recommendations at the point of care. METHODS: The objectives of this project were to develop and evaluate the usability of two symptom management algorithms (constipation and fatigue) across the trajectory of cancer care in patients with active disease treated in comprehensive or community cancer care settings to surveillance of cancer survivors in primary care practices. A modified ADAPTE process was used to develop algorithms based on national CPGs. Usability testing involved semi-structured interviews with clinicians from varied care settings, including comprehensive and community cancer centers, and primary care. The transcripts were analyzed with MAXQDA using Braun and Clarke's thematic analysis method. A cross tabs analysis was also performed to assess the prevalence of themes and subthemes by cancer care setting. RESULTS: A total of 17 clinicians (physicians, nurse practitioners, and physician assistants) were interviewed for usability testing. Three main themes emerged: (1) Algorithms as useful, (2) Symptom management differences, and (3) Different target end-users. The cross-tabs analysis demonstrated differences among care trajectories and settings that originated in the Symptom management differences theme. The sub-themes of "Differences between diseases" and "Differences between care trajectories" originated from participants working in a comprehensive cancer center, which tends to be disease-specific locations for patients on active treatment. Meanwhile, participants from primary care identified the sub-theme of "Differences in settings," indicating that symptom management strategies are care setting specific. CONCLUSIONS: While CDS can help promote evidence-based symptom management, systems providing care recommendations need to be specifically developed to fit patient characteristics and clinical context. Findings suggest that one set of algorithms will not be applicable throughout the entire cancer trajectory. Unique CDS for symptom management will be needed for patients who are cancer survivors being followed in primary care settings.

Impact of bariatric surgery on oral anticancer drugs: an analysis of real-world data.

PURPOSE: The number of patients with bariatric surgery who receive oral anticancer drugs is rising. Bariatric surgery may affect the absorption of oral anticancer drugs. Strikingly, no specific drug dosing recommendations are available. We aim to provide practical recommendations on the application of oral anticancer drugs in patients who underwent bariatric surgery. METHODS: Patients with any kind of bariatric surgery were extracted retrospectively in a comprehensive cancer center. In addition, a flowchart was proposed to assess the risk of inadequate exposure to oral anticancer drugs in patients who underwent bariatric surgery. Subsequently, the flowchart was evaluated retrospectively using routine Therapeutic drug monitoring (TDM) samples. RESULTS: In our analysis, 571 cancer patients (0.4% of 140.000 treated or referred patients) had previous bariatric surgery. Of these patients, 78 unique patients received 152 oral anticancer drugs equaling an overall number of 30 unique drugs. The 30 different prescribed oral anticancer drugs were categorized as low risk (13%), medium risk (67%), and high risk (20%) of underdosing. TDM plasma samples of 25 patients (82 samples) were available, of which 21 samples post-bariatric surgery (25%) were below the target value. CONCLUSIONS: The proposed flowchart can support optimizing the treatment with orally administered anticancer drugs in patients who underwent bariatric surgery. We recommend performing TDM in drugs that belong to BCS classes II, III, or IV. If more risk factors are present in BCS classes II or IV, a priori switches to other drugs may be advised. In specific cases, higher dosages can be provided from the start (e.g., tamoxifen).

A Nationwide Chronic Disease Management Solution via Clinical Decision Support Services: Software Development and Real-Life Implementation Report.

BACKGROUND: The increasing population of older adults has led to a rise in the demand for health care services, with chronic diseases being a major burden. Person-centered integrated care is required to address these challenges; hence, the Turkish Ministry of Health has initiated strategies to implement an integrated health care model for chronic disease management. We aim to present the design, development, nationwide implementation, and initial performance results of the national Disease Management Platform (DMP). OBJECTIVE: This paper's objective is to present the design decisions taken and technical solutions provided to ensure successful nationwide implementation by addressing several challenges, including interoperability with existing IT systems, integration with clinical workflow, enabling transition of care, ease of use by health care professionals, scalability, high performance, and adaptability. METHODS: The DMP is implemented as an integrated care solution that heavily uses clinical decision support services to coordinate effective screening and management of chronic diseases in adherence to evidence-based clinical guidelines and, hence, to increase the quality of health care delivery. The DMP is designed and implemented to be easily integrated with the existing regional and national health IT systems via conformance to international health IT standards, such as Health Level Seven Fast Healthcare Interoperability Resources. A repeatable cocreation strategy has been used to design and develop new disease modules to ensure extensibility while ensuring ease of use and seamless integration into the regular clinical workflow during patient encounters. The DMP is horizontally scalable in case of high load to ensure high performance. RESULTS: As of September 2023, the DMP has been used by 25,568 health professionals to perform 73,715,269 encounters for 16,058,904 unique citizens. It has been used to screen and monitor chronic diseases such as obesity, cardiovascular risk, diabetes, and hypertension, resulting in the diagnosis of 3,545,573 patients with obesity, 534,423 patients with high cardiovascular risk, 490,346 patients with diabetes, and 144,768 patients with hypertension. CONCLUSIONS: It has been demonstrated that the platform can scale horizontally and efficiently provides services to thousands of family medicine practitioners without performance problems. The system seamlessly interoperates with existing health IT solutions and runs as a part of the clinical workflow of physicians at the point of care. By automatically accessing and processing patient data from various sources to provide personalized care plan guidance, it maximizes the effect of evidence-based decision support services by seamless integration with point-of-care electronic health record systems. As the system is built on international code systems and standards, adaptation and deployment to additional regional and national settings become easily possible. The nationwide DMP as an integrated care solution has been operational since January 2020, coordinating effective screening and management of chronic diseases in adherence to evidence-based clinical guidelines.

Risk Factors for Nondiagnostic Imaging in a Real-World Deployment of Artificial Intelligence Diabetic Retinal Examinations in an Integrated Healthcare System: Maximizing Workflow Efficiency Through Predictive Dilation.

OBJECTIVE: In the pivotal clinical trial that led to Food and Drug Administration De Novo "approval" of the first fully autonomous artificial intelligence (AI) diabetic retinal disease diagnostic system, a reflexive dilation protocol was used. Using real-world deployment data before implementation of reflexive dilation, we identified factors associated with nondiagnostic results. These factors allow a novel predictive dilation workflow, where patients most likely to benefit from pharmacologic dilation are dilated a priori to maximize efficiency and patient satisfaction. METHODS: Retrospective review of patients who were assessed with autonomous AI at Johns Hopkins Medicine (8/2020 to 5/2021). We constructed a multivariable logistic regression model for nondiagnostic results to compare characteristics of patients with and without diagnostic results, using adjusted odds ratio (aOR). P < .05 was considered statistically significant. RESULTS: Of 241 patients (59% female; median age = 59), 123 (51%) had nondiagnostic results. In multivariable analysis, type 1 diabetes (T1D, aOR = 5.82, 95% confidence interval [CI]: 1.45-23.40, P = .01), smoking (aOR = 2.86, 95% CI: 1.36-5.99, P = .005), and age (every 10-year increase, aOR = 2.12, 95% CI: 1.62-2.77, P < .001) were associated with nondiagnostic results. Following feature elimination, a predictive model was created using T1D, smoking, age, race, sex, and hypertension as inputs. The model showed an area under the receiver-operator characteristics curve of 0.76 in five-fold cross-validation. CONCLUSIONS: We used factors associated with nondiagnostic results to design a novel, predictive dilation workflow, where patients most likely to benefit from pharmacologic dilation are dilated a priori. This new workflow has the potential to be more efficient than reflexive dilation, thus maximizing the number of at-risk patients receiving their diabetic retinal examinations.

Implementation and assessment of an electronic clinical decision support tool to notify clinicians of critical hyperbilirubinemia in preterm neonates less than 35 weeks' gestation.

OBJECTIVES: Critical hyperbilirubinemia in preterm neonates, a condition requiring greater attention, is treated with phototherapy or exchange transfusion when bilirubin results exceed gestational age and age-specific medical decision levels (MDLs) to prevent bilirubin-induced neurologic damage. Conventional evaluation involves multiple manual steps and is poised to inconsistencies and delays. METHODS: We designed and implemented an electronic clinical decision support (CDS) tool to identify and alert neonatal intensive care unit clinicians of critical hyperbilirubinemia with a SmartZone alert. We evaluated the performance of our manual evaluation workflow, the accuracy of the electronic CDS tool, and the outcome of the electronic CDS tool to reduce the time to place orders for interventions. RESULTS: Among the 22 patients who met the criteria to have phototherapy ordered before implementing the electronic CDS tool, 20 (90%) had phototherapy ordered. Fourteen (70%) phototherapy orders were placed less than 24 hours, 4 phototherapy orders were placed 24 to 72 hours, and 2 orders were placed more than 72 hours after bilirubin results exceeded the corresponding MDLs. Among the 15 patients who met the criteria to have phototherapy ordered after implementing the electronic CDS tool, all (100%) received phototherapy orders, with 14 (93%) placed less than 24 hours and 1 order placed less than 48 hours. The electronic CDS tool identified all eligible patients correctly. The proportion of phototherapy ordered less than 24 hours increased from 70% to 93% after the implementation of the electronic CDS tool. CONCLUSIONS: The electronic CDS tool promoted more appropriate and timely intervention orders to manage critical hyperbilirubinemia in preterm neonates.

Utilizing Learn-to-Rank Systems for More Effective Diagnosis in Rural Family Medicine.

This editorial investigates the development and efficacy of Japanese learn-to-rank approach systems in family medicine, emphasizing their establishment by Dr. Keijiro Torigoe and their significance in rural community hospitals. Initiated in 1977, Dr. Torigoe's innovative system integrated international medical knowledge with technology, yielding a comprehensive database of 7,000 registered diseases. These learn-to-rank approaches, notably the listwise method, address technological gaps in extracting data on differential diseases and enhance the predictive performance of clinical decision support systems, offering a holistic, culturally resonant healthcare approach. They are especially vital in rural medicine, aiding in managing the volatility, uncertainty, complexity, and ambiguity prevalent among older patients, streamlining diagnoses, and improving healthcare delivery in resource-constrained settings. In conclusion, integrating Japanese learn-to-rank approach systems is pivotal in revolutionizing disease diagnosis, catering to diverse rural health needs, and fostering sustainability in rural healthcare systems. By harmonizing medical insights with innovation, they demonstrate the potential for a comprehensive and contextually relevant approach to healthcare in Japan.

Model Clinic to Increase Preventive Screenings Among Patients With Physical Disabilities: Protocol for a Mixed Methods Intervention Pilot Study.

BACKGROUND: People with physical disabilities often experience premature multimorbidity and adverse health events. A tailored primary care approach for this vulnerable population that also accounts for social and functional risk factors could promote healthier aging and more equitable health care. OBJECTIVE: This project will evaluate the implementation of a health program designed for people with physical disabilities. The proposed evaluation result is to generate the first best-practice protocol focused specifically on developing primary care to help reduce preventable causes of morbidity and improve functioning among people with physical disabilities. METHODS: We will design and implement a pilot health program for people with physical disabilities at a primary care clinic within Michigan Medicine. The health program for people with physical disabilities will be an integrated intervention involving a tailored best practice alert designed to prompt family medicine providers to screen and monitor for common, preventable health conditions. The program will also collect social and functional status information to determine the patient's need for further care coordination and support. Adult participants from this clinic with identified physical disabilities will be targeted for potential enrollment. To create a quasi-experimental setting, a separate departmental clinic will serve as a control site for comparison purposes. A quantitative analysis to estimate the treatment effect of implementing this health program will be conducted using a difference-in-differences approach. Outcomes of interest will include the use of preventative services (eg, hemoglobin A1c for diabetes screening), social work assistance, and emergency and hospital services. These data will be extracted from electronic health records. Time-invariant covariates, particularly sociodemographic covariates, will be included in the models. A qualitative analysis of patient and health care provider interviews will also be completed to assess the effect of the health program. Patient Health Questionnaire-9 and Generalized Anxiety Disorder 7-item scores will be assessed to both screen for depression and anxiety as well as explore program impacts related to addressing health and functioning needs related to physical disabilities in a primary care setting. These will be summarized through descriptive analyses. RESULTS: This study was funded in September 2018, data collection started in September 2021, and data collection is expected to be concluded in September 2023. CONCLUSIONS: This study is a mixed methods evaluation of the effectiveness of an integrated health program designed for people with physical disabilities, based on a quasi-experimental comparison between an intervention and a control clinic site. The intervention will be considered successful if it leads to improvements in greater use of screening and monitoring for preventable health conditions, increased social worker referrals to assist with health and functioning needs, and improvements in emergency and hospital-based services. The findings will help inform best practices for people with physical disabilities in a primary care setting. INTERNATIONAL REGISTERED REPORT IDENTIFIER (IRRID): DERR1-10.2196/50105.

Comparative effectiveness of generic commercial versus locally customized clinical decision support tools to reduce prescription of nonsteroidal anti-inflammatory drugs for patients with heart failure.

OBJECTIVE: To compare the effectiveness of 2 clinical decision support (CDS) tools to avoid prescription of nonsteroidal anti-inflammatory drugs (NSAIDs) in patients with heart failure (HF): a "commercial" and a locally "customized" alert. METHODS: We conducted a retrospective cohort study of 2 CDS tools implemented within a large integrated health system. The commercial CDS tool was designed according to third-party drug content and EHR vendor specifications. The customized CDS tool underwent a user-centered design process informed by implementation science principles, with input from a cross disciplinary team. The customized CDS tool replaced the commercial CDS tool. Data were collected from the electronic health record via analytic reports and manual chart review. The primary outcome was effectiveness, defined as whether the clinician changed their behavior and did not prescribe an NSAID. RESULTS: A random sample of 366 alerts (183 per CDS tool) was evaluated that represented 355 unique patients. The commercial CDS tool was effective for 7 of 172 (4%) patients, while the customized CDS tool was effective for 81 of 183 (44%) patients. After adjusting for age, chronic kidney disease, ejection fraction, NYHA class, concurrent prescription of an opioid or acetaminophen, visit type (inpatient or outpatient), and clinician specialty, the customized alerts were at 24.3 times greater odds of effectiveness compared to the commercial alerts (OR: 24.3 CI: 10.20-58.06). CONCLUSION: Investing additional resources to customize a CDS tool resulted in a CDS tool that was more effective at reducing the total number of NSAID orders placed for patients with HF compared to a commercially available CDS tool.

Who responds to a multi-component treatment for cannabis use disorder? Using multivariable and machine learning models to classify treatment responders and non-responders.

BACKGROUND AND AIMS: Treatments for cannabis use disorder (CUD) have limited efficacy and little is known about who responds to existing treatments. Accurately predicting who will respond to treatment can improve clinical decision-making by allowing clinicians to offer the most appropriate level and type of care. This study aimed to determine whether multivariable/machine learning models can be used to classify CUD treatment responders versus non-responders. METHODS: This secondary analysis used data from a National Drug Abuse Treatment Clinical Trials Network multi-site outpatient clinical trial in the United States. Adults with CUD (n = 302) received 12 weeks of contingency management, brief cessation counseling and were randomized to receive additionally either (1) N-Acetylcysteine or (2) placebo. Multivariable/machine learning models were used to classify treatment responders (i.e. two consecutive negative urine cannabinoid tests or a 50% reduction in days of use) versus non-responders using baseline demographic, medical, psychiatric and substance use information. RESULTS: Prediction performance for various machine learning and regression prediction models yielded area under the curves (AUCs) >0.70 for four models (0.72-0.77), with support vector machine models having the highest overall accuracy (73%; 95% CI = 68-78%) and AUC (0.77; 95% CI = 0.72, 0.83). Fourteen variables were retained in at least three of four top models, including demographic (ethnicity, education), medical (diastolic/systolic blood pressure, overall health, neurological diagnosis), psychiatric (depressive symptoms, generalized anxiety disorder, antisocial personality disorder) and substance use (tobacco smoker, baseline cannabinoid level, amphetamine use, age of experimentation with other substances, cannabis withdrawal intensity) characteristics. CONCLUSIONS: Multivariable/machine learning models can improve on chance prediction of treatment response to outpatient cannabis use disorder treatment, although further improvements in prediction performance are likely necessary for decisions about clinical care.

To scan or not to scan: Use of transient elastography in an integrated health system.

BACKGROUND: Non-invasive tests, such as Fibrosis-4 index and transient elastography (commonly FibroScan), are utilized in clinical pathways to risk stratify and diagnose non-alcoholic fatty liver disease (NAFLD). In 2018, a clinical decision support tool (CDST) was implemented to guide primary care providers (PCPs) on use of FibroScan for NAFLD. AIM: To analyze how this CDST impacted health care utilization and patient outcomes. METHODS: We performed a retrospective review of adults who had FibroScan for NAFLD indication from January 2015 to December 2017 (pre-CDST) or January 2018 to December 2020 (post-CDST). Outcomes included FibroScan result, laboratory tests, imaging studies, specialty referral, patient morbidity and mortality. RESULTS: We identified 958 patients who had FibroScan, 115 before and 843 after the CDST was implemented. The percentage of FibroScans ordered by PCPs increased from 33% to 67.1%. The percentage of patients diagnosed with early F1 fibrosis, on a scale from F0 to F4, increased from 7.8% to 14.2%. Those diagnosed with advanced F4 fibrosis decreased from 28.7% to 16.5%. There were fewer laboratory tests, imaging studies and biopsy after the CDST was implemented. Though there were more specialty referrals placed after the CDST was implemented, multivariate analysis revealed that healthcare utilization aligned with fibrosis score, whereby patients with more advanced disease had more referrals. Very few patients were hospitalized or died. CONCLUSION: This CDST empowered PCPs to diagnose and manage patients with NAFLD with appropriate allocation of care towards patients with more advanced disease.

Effect of botanical drugs in improving symptoms of hypertensive nephropathy: Analysis of real-world data, retrospective cohort, network, and experimental assessment.

Background/aim: Hypertensive nephropathy (HN) is a common complication of hypertension. Traditional Chinese medicine has long been used in the clinical treatment of Hypertensive nephropathy. However, botanical drug prescriptions have not been summarized. The purpose of this study is to develop a prescription for improving hypertensive nephropathy, explore the evidence related to clinical application of the prescription, and verify its molecular mechanism of action. Methods: In this study, based on the electronic medical record data on Hypertensive nephropathy, the core botanical drugs and patients' symptoms were mined using the hierarchical network extraction and fast unfolding algorithm, and the protein interaction network between botanical drugs and Hypertensive nephropathy was established. The K-nearest neighbors (KNN) model was used to analyze the clinical and biological characteristics of botanical drug compounds to determine the effective compounds. Hierarchical clustering was used to screen for effective botanical drugs. The clinical efficacy of botanical drugs was verified by a retrospective cohort. Animal experiments were performed at the target and pathway levels to analyze the mechanism. Results: A total of 14 botanical drugs and five symptom communities were obtained from real-world clinical data. In total, 76 effective compounds were obtained using the K-nearest neighbors model, and seven botanical drugs were identified as Gao Shen Formula by hierarchical clustering. Compared with the classical model, the Area under the curve (AUC) value of the K-nearest neighbors model was the best; retrospective cohort verification showed that Gao Shen Formula reduced serum creatinine levels and Chronic kidney disease (CKD) stage [OR = 2.561, 95% CI (1.025-6.406), p < 0.05]. With respect to target and pathway enrichment, Gao Shen Formula acts on inflammatory factors such as TNF-α, IL-1ß, and IL-6 and regulates the NF-κB signaling pathway and downstream glucose and lipid metabolic pathways. Conclusion: In the retrospective cohort, we observed that the clinical application of Gao Shen Formula alleviates the decrease in renal function in patients with hypertensive nephropathy. It is speculated that Gao Shen Formula acts by reducing inflammatory reactions, inhibiting renal damage caused by excessive activation of the renin-angiotensin-aldosterone system, and regulating energy metabolism.

The voice of healthcare: introducing digital decision support systems into clinical practice - a qualitative study.

BACKGROUND: There is a need to accelerate digital transformation in healthcare to meet increasing needs and demands. The accuracy of medical digital diagnosis tools is improving. The introduction of new technology in healthcare can however be challenging and it is unclear how it should be done to reach desired results. The aim of this study was to explore perceptions and experiences of introducing new Information Technology (IT) in a primary healthcare organisation, exemplified with a Clinical Decision Support System (CDSS) for malignant melanoma. METHODS: A qualitative interview-based study was performed in Region Stockholm, Sweden, with fifteen medical doctors representing three different organisational levels - primary care physician, primary healthcare centre manager, and regional manager/chief medical officer. In addition, one software provider was included. Interview data were analysed according to content analysis. RESULTS: One central theme "Introduction of digital CDSS in primary healthcare requires a multidimensional perspective and handling" along with seven main categories and thirty-three subcategories emerged from the analysis. Digital transformation showed to be key for current healthcare providers to stay relevant and competitive. However, healthcare represents a closed community, very capable but with lack of time, fostered to be sceptical to new why change needs to bring true value and be inspired by people with medical background to motivate the powerful frontline. CONCLUSIONS: This qualitative study revealed structured information of what goes wrong and right and what needs to be considered when driving digital change in primary care organisations. The task shows to be complex and the importance of listening to the voice of healthcare is valuable for understanding the conditions that need to be fulfilled when adopting new technology into a healthcare organization. By considering the findings of this study upcoming digital transformations can improve their success-rate. The information may also be used in developing a holistic approach or framework model, adapted to primary health care, that can support and accelerate the needed digitalization in healthcare as such.

A Novel Approach to Delivering Evidence-based, High-quality Care in Psychiatry Through an Electronic Integrated Care Pathway (eICP) Pilot.

Integrated care pathways (ICPs) are evidence-based decision support tools intended to reduce variation and improve quality of care. Historically, adoption of ICPs has been difficult to measure, as the pathways were outside of the electronic health record (EHR), where care delivery documentation and orders were completed. This Technology Column describes the innovative development and implementation of a diagnosis specific electronic ICP that directly embeds pathway steps into an EHR to facilitate order sets, clinical decision-making, and usage tracking. The pathway was implemented at a seven-hospital academic medical center, and details the technology, team structure, early adoption results, and future directions. As such, the importance of investing and organizing resources to create an eICP (e.g., time, technology, and specialized teams) to provide a user-friendly experience to support early adoption is underscored. Preliminary findings show that the eICP had consistent use in the first year of implementation. This manuscript is intended to serve as a practical guide to build eICPs within behavioral health service areas across institutions.

Pediatric considerations for pharmacogenetic selective serotonin reuptake inhibitors clinical decision support.

Pharmacogenetic testing for psychiatry is growing at a rapid pace, with multiple sites utilizing results to help clinical decision-making. Genotype-guided dosing and drug selection have been implemented at several sites, including Vanderbilt University Medical Center, where clinical decision support (CDS) based on pharmacogenetic results went live for selective serotonin reuptake inhibitors in 2020 for both adult and pediatric patients. Effective and appropriate implementation of CYP2D6- and CYP2C19-guided CDS for the pediatric population requires consideration of the evidence for the pharmacogenetic associations, medication indications, and appropriate alternative therapies to be used when a pharmacogenetic contraindication is identified. In this article, we review these pediatric pharmacogenetic considerations for selective serotonin reuptake inhibitor CDS. We include a case study, the current literature supporting clinical recommendations, considerations when designing pediatric CDS, future implications, and examples of sertraline, (es)citalopram, paroxetine, and fluvoxamine alerts.

Pharmacogenomics driven decision support prototype with machine learning: A framework for improving patient care.

Introduction: A growing number of healthcare providers make complex treatment decisions guided by electronic health record (EHR) software interfaces. Many interfaces integrate multiple sources of data (e.g., labs, pharmacy, diagnoses) successfully, though relatively few have incorporated genetic data. Method: This study utilizes informatics methods with predictive modeling to create and validate algorithms to enable informed pharmacogenomic decision-making at the point of care in near real-time. The proposed framework integrates EHR and genetic data relevant to the patient's current medications including decision support mechanisms based on predictive modeling. We created a prototype with EHR and linked genetic data from the Department of Veterans Affairs (VA), the largest integrated healthcare system in the US. The EHR data included diagnoses, medication fills, and outpatient clinic visits for 2,600 people with HIV and matched uninfected controls linked to prototypic genetic data (variations in single or multiple positions in the DNA sequence). We then mapped the medications that patients were prescribed to medications defined in the drug-gene interaction mapping of the Clinical Pharmacogenomics Implementation Consortium's (CPIC) level A (i.e., sufficient evidence for at least one prescribing action) guidelines that predict adverse events. CPIC is a National Institute of Health funded group of experts who develop evidence based pharmacogenomic guidelines. Preventable adverse events (PAE) can be defined as a harmful outcome from an intervention that could have been prevented. For this study, we focused on potential PAEs resulting from a medication-gene interaction. Results: The final model showed AUC scores of 0.972 with an F1 score of 0.97 with genetic data as compared to 0.766 and 0.73 respectively, without genetic data integration. Discussion: Over 98% of people in the cohort were on at least one medication with CPIC level a guideline in their lifetime. We compared predictive power of machine learning models to detect a PAE between five modeling methods: Random Forest, Support Vector Machine (SVM), Extreme Gradient Boosting (XGBoost), K Nearest neighbors (KNN), and Decision Tree. We found that XGBoost performed best for the prototype when genetic data was added to the framework and improved prediction of PAE. We compared area under the curve (AUC) between the models in the testing dataset.

The Impact of a Clinical Decision Support System for Addressing Physical Activity and Healthy Eating During Smoking Cessation Treatment: Hybrid Type I Randomized Controlled Trial.

BACKGROUND: People who smoke have other risk factors for chronic diseases, such as low levels of physical activity and poor diet. Clinical decision support systems (CDSSs) might help health care practitioners integrate interventions for diet and physical activity into their smoking cessation programming but could worsen quit rates. OBJECTIVE: The aims of this study are to assess the effects of the addition of a CDSS for physical activity and diet on smoking cessation outcomes and to assess the implementation of the study. METHODS: We conducted a pragmatic hybrid type I effectiveness-implementation trial with 232 team-based primary care practices in Ontario, Canada, from November 2019 to May 2021. We used a 2-arm randomized controlled trial comparing a CDSS addressing physical activity and diet to treatment as usual and used the Reach, Effectiveness, Adoption, Implementation, and Maintenance framework to measure implementation outcomes. The primary outcome was self-reported 7-day tobacco abstinence at 6 months. RESULTS: We enrolled 5331 participants in the study. Of these, 2732 (51.2%) were randomized to the intervention group and 2599 (48.8%) to the control group. At the 6-month follow-up, 29.7% (634/2137) of respondents in the intervention arm and 27.3% (552/2020) in the control arm reported abstinence from tobacco. After multiple imputation, the absolute group difference was 2.1% (95% CI -0.5 to 4.6; F1,1000.42=2.43; P=.12). Mean exercise minutes changed from 32 (SD 44.7) to 110 (SD 196.1) in the intervention arm and from 32 (SD 45.1) to 113 (SD 195.1) in the control arm (group effect: B=-3.7 minutes; 95% CI -17.8 to 10.4; P=.61). Servings of fruit and vegetables changed from 2.64 servings to 2.42 servings in the intervention group and from 2.52 servings to 2.45 servings in the control group (incidence rate ratio for intervention group=0.98; 95% CI 0.93-1.02; P=.35). CONCLUSIONS: A CDSS for physical activity and diet may be added to a smoking cessation program without affecting the outcomes. Further research is needed to improve the impact of integrated health promotion interventions in primary care smoking cessation programs. TRIAL REGISTRATION: ClinicalTrials.gov NCT04223336 https://www.clinicaltrials.gov/ct2/show/NCT04223336. INTERNATIONAL REGISTERED REPORT IDENTIFIER (IRRID): RR2-10.2196/19157.

Use of Laughter for the Detection of Parkinson's Disease: Feasibility Study for Clinical Decision Support Systems, Based on Speech Recognition and Automatic Classification Techniques.

Parkinson's disease (PD) is an incurable neurodegenerative disorder which affects over 10 million people worldwide. Early detection and correct evaluation of the disease is critical for appropriate medication and to slow the advance of the symptoms. In this scenario, it is critical to develop clinical decision support systems contributing to an early, efficient, and reliable diagnosis of this illness. In this paper we present a feasibility study for a clinical decision support system for the diagnosis of PD based on the acoustic characteristics of laughter. Our decision support system is based on laugh analysis with speech recognition methods and automatic classification techniques. We evaluated different cepstral coefficients to identify laugh characteristics of healthy and ill subjects combined with machine learning classification models. The decision support system reached 83% accuracy rate with an AUC value of 0.86 for PD-healthy laughs classification in a database of 20,000 samples randomly generated from a pool of 120 laughs from healthy and PD subjects. Laughter could be employed for the efficient and reliable detection of PD; such a detection system can be achieved using speech recognition and automatic classification techniques; a clinical decision support system can be built using the above techniques. Significance: PD clinical decision support systems for the early detection of the disease will help to improve the efficiency of available and upcoming therapeutic treatments which, in turn, would improve life conditions of the affected people and would decrease costs and efforts in public and private healthcare systems.

A systematic literature review of AI-based digital decision support systems for post-traumatic stress disorder.

Objective: Over the last decade, an increase in research on medical decision support systems has been observed. However, compared to other disciplines, decision support systems in mental health are still in the minority, especially for rare diseases like post-traumatic stress disorder (PTSD). We aim to provide a comprehensive analysis of state-of-the-art digital decision support systems (DDSSs) for PTSD. Methods: Based on our systematic literature review of DDSSs for PTSD, we created an analytical framework using thematic analysis for feature extraction and quantitative analysis for the literature. Based on this framework, we extracted information around the medical domain of DDSSs, the data used, the technology used for data collection, user interaction, decision-making, user groups, validation, decision type and maturity level. Extracting data for all of these framework dimensions ensures consistency in our analysis and gives a holistic overview of DDSSs. Results: Research on DDSSs for PTSD is rare and primarily deals with the algorithmic part of DDSSs (n = 17). Only one DDSS was found to be a usable product. From a data perspective, mostly checklists or questionnaires were used (n = 9). While the median sample size of 151 was rather low, the average accuracy was 82%. Validation, excluding algorithmic accuracy (like user acceptance), was mostly neglected, as was an analysis concerning possible user groups. Conclusion: Based on a systematic literature review, we developed a framework covering all parts (medical domain, data used, technology used for data collection, user interaction, decision-making, user groups, validation, decision type and maturity level) of DDSSs. Our framework was then used to analyze DDSSs for post-traumatic stress disorder. We found that DDSSs are not ready-to-use products but are mostly algorithms based on secondary datasets. This shows that there is still a gap between technical possibilities and real-world clinical work.