Healthcare Tools Research Articles

Identifying unique spectral fingerprints in cough sounds for diagnosing respiratory ailments

Coughing, a prevalent symptom of many illnesses, including COVID-19, has led researchers to explore the potential of cough sound signals for cost-effective disease diagnosis. Traditional diagnostic methods, which can be expensive and require specialized personnel, contrast with the more accessible smartphone analysis of coughs. Typically, coughs are classified as wet or dry based on their phase duration. However, the utilization of acoustic analysis for diagnostic purposes is not widespread. Our study examined cough sounds from 1183 COVID-19-positive patients and compared them with 341 non-COVID-19 cough samples, as well as analyzing distinctions between pneumonia and asthma-related coughs. After rigorous optimization across frequency ranges, specific frequency bands were found to correlate with each respiratory ailment. Statistical separability tests validated these findings, and machine learning algorithms, including linear discriminant analysis and k-nearest neighbors classifiers, were employed to confirm the presence of distinct frequency bands in the cough signal power spectrum associated with particular diseases. The identification of these acoustic signatures in cough sounds holds the potential to transform the classification and diagnosis of respiratory diseases, offering an affordable and widely accessible healthcare tool.

A Systematic Review and Meta-Analysis of Artificial Intelligence Tools in Medicine and Healthcare: Applications, Considerations, Limitations, Motivation and Challenges.

Artificial intelligence (AI) has emerged as a transformative force in various sectors, including medicine and healthcare. Large language models like ChatGPT showcase AI's potential by generating human-like text through prompts. ChatGPT's adaptability holds promise for reshaping medical practices, improving patient care, and enhancing interactions among healthcare professionals, patients, and data. In pandemic management, ChatGPT rapidly disseminates vital information. It serves as a virtual assistant in surgical consultations, aids dental practices, simplifies medical education, and aids in disease diagnosis. A total of 82 papers were categorised into eight major areas, which are G1: treatment and medicine, G2: buildings and equipment, G3: parts of the human body and areas of the disease, G4: patients, G5: citizens, G6: cellular imaging, radiology, pulse and medical images, G7: doctors and nurses, and G8: tools, devices and administration. Balancing AI's role with human judgment remains a challenge. A systematic literature review using the PRISMA approach explored AI's transformative potential in healthcare, highlighting ChatGPT's versatile applications, limitations, motivation, and challenges. In conclusion, ChatGPT's diverse medical applications demonstrate its potential for innovation, serving as a valuable resource for students, academics, and researchers in healthcare. Additionally, this study serves as a guide, assisting students, academics, and researchers in the field of medicine and healthcare alike.

Human Freedom from Algorithmic Bias: What is the role of Accountability in addressing Health Disparities?

While there are many causes of health disparities, the application of Artificial Intelligence tools in healthcare may have mixed results. The purpose of this paper is to investigate the role of human freedoms and accountability to achieving digital inclusion. It discovers the role of algorithmic bias in mediating the relationship between human freedom and mobile health. The following research questions are investigated: 1) How do human freedoms effect digital inclusion and mobile health? 2) Do human freedoms effect mobile health? And 3) Does AI accountability mediate the relationship between human freedoms and mobile health? The findings suggest that human freedoms are central to digital inclusion and mobile health. Accountability does affect the extent to which digital inclusion can be achieved through human freedoms. AI accountability significantly mediates the relationship between human freedoms and the mobile index. This offers an important contribution in uncovering the role of algorithmic bias in human freedom and mobile health, and of accountability between human freedom and digital inclusion.

Evaluation of the accuracy and quality of ChatGPT-4 responses for hyperparathyroidism patients discussed at multidisciplinary endocrinology meetings.

Chat Generative Pre-trained Transformer (ChatGPT) is now utilized in various fields of healthcare in order to obtain answers to questions related to healthcare-related problems and to evaluate available information. Primary hyperparathyroidism is a common endocrine disorder. We aimed to evaluate the accuracy and quality of ChatGPT's responses to questions specific to hyperparathyroidism cases discussed at multidisciplinary endocrinology meetings. ChatGPT-4 was asked to respond to 10 hyperparathyroidism cases evaluated at multidisciplinary endocrinology meetings. The accuracy, completeness, and quality of the responses were scored independently by two endocrinologists. Accuracy and completeness were evaluated on the Likert scale, and quality was evaluated on the global quality scale (GQS). No misleading information was detected in the responses. In terms of diagnosis, the mean accuracy scores (ranging from 1 to 5) were 4.9 ± 0.1 and the mean completeness scores (ranging from 1 to 3) were 3.0. In the responses given in terms of further examination, the mean accuracy and completeness scores were 4.8 ± 0.13 and 2.6 ± 0.16, respectively. The mean accuracy and completeness scores for treatment recommendations were 4.9 ± 0.1 and 2.4 ± 0.16, respectively. The GQS evaluation result was 80% high quality and 20% medium quality. In this study, the accuracy and quality rates of ChatGPT-4 were generally high in responding to questions as to hyperparathyroidism patients. It can be concluded that artificial intelligence may serve as a valuable tool in healthcare. However, the limitations and risks of ChatGPT should also be evaluated.

Privacy preserving vertical distributed learning for health data

Federated learning has become a pivotal tool in healthcare, enabling valuable insights to be gleaned from disparate datasets held by cautious data owners concerned about data privacy. This method involves the analysis of data from diverse locations, which is subsequently aggregated and trained on a central server. Data distribution can occur vertically or horizontally in this decentralized setup. In our approach, we employ a unique vertical partition learning process, segmenting data by characteristics or columns for each record across all local sites, known as Vertical Distributed Learning or features distributed machine learning. Our collaborative learning approach utilizes Stochastic Gradient Descent to collectively learn from each local site and compute the final result on a central server. Notably, during the training phase, no raw data or model parameters are exchanged; only local prediction results are shared and aggregated. Yet, sharing local prediction results raises privacy concerns, which we mitigate by introducing noise into the local results using a Differential Privacy algorithm. This paper introduces a robust vertical distributed learning system that emphasizes user privacy for healthcare data. To assess our approach, we conducted experiments using the sensitive healthcare data in the Medical Information Mart for Intensive Care-Ⅲ dataset and the publicly available Adult dataset. Our experimental results demonstrate that our approach achieves an accuracy level similar to that of a fully centralized model, significantly surpassing training based solely on local features. Consequently, our solution offers an effective federated learning approach for healthcare, preserving data locality and privacy while efficiently harnessing vertically partitioned data.

Consensus on criteria and minimum data set for the referral of candidate patients for home enteral nutrition teleconsultation: TELENUT project

Introduction: teleconsultation is a useful healthcare tool in the multidisciplinary management of patients with indications of home enteral nutrition (HEN). The use of different teleconsultation platforms, as it happens in the Andalusian Health System (SAS), results in heterogeneous referral processes between Primary Care and hospital services in the same region. Objectives: to establish a consensus on patient profiles and the minimum data set necessary to guarantee an adequate referral to NED teleconsultation regardless of the existing platform. These agreed aspects in Andalusia can serve as a reference in other regions. Methods: three consecutive steps were followed: a) non-systematic review of the indexed literature on teleconsultation in clinical nutrition in Spain; b) survey to know the implementation and unmet needs of teleconsultation platforms in Andalusian public hospitals; and c) working meetings and consensus of 14 health professionals of Primary Care (n = 4) and endocrinology and hospital clinical nutrition (n = 10). Results: three referral forms were agreed in which three patient profiles were defined, with the corresponding minimum set of data necessary to request NED teleconsultation. The Primary Care team should provide this set of data to the clinical nutrition specialist via a teleconsultation platform, implemented in the SAS. Conclusions: three agreed forms between healthcare professionals involved in the referral process serve to standardize the request for teleconsultation of NED between healthcare teams based on patient profiles.

Prevalence of Hyperuricemia and Its Risk Factors Among Residents of a Rural Field Practice Area in Belagavi, India

Background: Serum Uric Acid (SUA) is often overlooked in primary healthcare, primarily associated with Gouty Arthritis. However, the literature indicates its links to dyslipidaemia, cardiovascular diseases, hypertension, and metabolic syndrome. This study aimed to determine the prevalence of hyperuricemia and its associated risk factors among rural residents. Methodology: A cross-sectional study was conducted from January to May 2023, involving 300 individuals aged ≥30 during routine NCD screening. Data on socio-demographics, meat consumption habits, thiazide diuretic usage, and anthropometric measurements were collected. Blood samples were obtained for SUA analysis after informed consent. Results: Participants had a median age of 45 years, BMI of 25.41 kg/m², and SUA level of 5 mg/dl. The study revealed an overall hyperuricemia prevalence of 32.7%. While no significant associations were found between hyperuricemia and the variables analysed, a statistically significant positive linear correlation emerged between SUA levels and waist or hip circumference. Binary logistic regression showed a significant association between hyperuricemia and the frequency of meat consumption. Conclusion: This study suggests that SUA can serve as a valuable tool in primary healthcare. Identifying and treating individuals with hyperuricemia, and educating those at risk, can help prevent future vascular events.

The Use of Instagram Among Dental Patients in Saudi Arabia.

The use of social media platforms has become increasingly popular in various professional fields, including dentistry. Among these platforms, Instagram has gained significant attention due to its visual nature and widespread popularity. The potential of Instagram as a tool for dental education, patient engagement, and professional networking has been the subject of several studies. This paper aims to investigate the use of Instagram among dental patients and examine its implications for the dental industry.. This cross-sectional study was conducted in Saudi Arabia between September and December 2023 and involved 478 anonymous dental patients who responded to survey invitations. The questionnaire addressed demographics, Instagram usage, and factors influencing the selection of a dentist. Data analysis employed IBM SPSS Statistics, presented as descriptive statistics and bivariate analysis. Of the respondents, 66.6% preferred Instagram to follow dentists. Demographically, younger age correlated with increased Instagram use. Over half used Instagram 2-3 times daily, with evenings being the preferred time. Notably, 48.3% visited a dentist's Instagram account. Recommendations, qualifications, and online reviews were crucial factors in choosing a dentist, while before-and-after photos, engagement, and content quality mattered on Instagram. Instagram serves as a valuable tool in oral healthcare, fostering patient engagement, education, and community building. Dental practitioners should leverage Instagram judiciously, emphasizing its supplementary role to professional consultations rather than a substitute for personalized dental advice.

Value-based healthcare in management of chronic back pain: A multidisciplinary- and lean-based approach.

Chronic back pain stands as the most common musculoskeletal disorder and a primary cause of disability in people under 45 years old. Multidisciplinary consultation offers an efficient approach to chronic back pain management compared to traditional therapeutic-rehabilitative paths. This paper aims to show the benefit of a diagnostic-therapeutic multidisciplinary program pathway for patients with chronic back pain. Twenty-six patients who underwent a second-level multidisciplinary consultation with a neurosurgeon and a pain therapist at our University Hospital were retrospectively identified from April 2023 to September 2023. The second-level multidisciplinary consultation is a second step consultation after a first consultation with a single specialist doctor (neurosurgeon, orthopedic, and pain therapist) who did not get the diagnosis and/or did not solve the painful symptom after medical or surgical treatment. Clinical outcomes, patient experience, and cost-effectiveness analysis were assessed using lean healthcare tools. With the introduction of second-level multidisciplinary consultation, patients were assessed by multiple physicians during a single visit, reducing the costs of individual visits, reducing the time to obtain the diagnosis, and facilitating early agreement on a diagnostic-therapeutic plan. The lean value-based healthcare approach showed an average of 45 working days lost per single patient and a total cost per single patient with chronic back pain of € 1069 for the national health system for an average Lead time of 18 months. Questionnaire analysis on service quality and utility, along with overall satisfaction, revealed excellent resolution of back pain in 53.8% of cases and partial resolution of back pain in 11.5% of cases after second-level multidisciplinary consultation. Our multidisciplinary approach to chronic back pain has significantly improved healthcare efficiency. This new proposed clinical model reduces waiting times and costs and improves patient experience by improving clinical outcomes in the management of chronic back pain.

CARCINOGENICITY OF IONIZING RADIATION: A LITERATURE REVIEW

Relevance: According to WHO, malignant neoplasms rank second in population mortality structure due to a constantly increasing influence of technogenic factors that have a direct carcinogenic effect on the body and suppress defense mechanisms. Ionizing radiation plays a special role in the development of cancer. It is used in industry, agriculture, medicine, and scientific research as a diagnostic tool in modern healthcare and radiation therapy for cancer treatment. The consequences of radiation influence are not only the result of a direct effect on the body but also a delayed one through generations of parents and grandparents. According to the radiobiological hypothesis, any level of radiation, no matter how small, poses a risk of long-term consequences, including cancer, in exposed people and their descendants of the first two generations. That is, cancerous tumors are likely consequences of the influence of radiation. Despite various theories of the biological effect of low doses of ionizing radiation, most authors attach primary importance to DNA damage in the manifestation of genetic effects (the concept of non-threshold mutational action).
 The study aimed to highlight the role of ionizing radiation in tumorigenesis.
 Methods: Data from MEDLINE, Embase, Scopus, PubMed, Cochrane Central Register of Controlled Trials was analyzed to select and analyze relevant information over the past 10 years using the keywords: gamma irradiation, spontaneous oncogenesis, prevention of oncogenesis.
 Results: Radiation exposure may increase the risk of cancer development due to epigenetic changes leading to increased genomic instability (GI) and/or specific suppression of tumor suppressor genes. Changes in the TP53 gene network expression occur; the most significant genes as predictors of carcinogenesis are ST13, IER3, BRCAI, LRDD, and MRAS. Epigenetic changes also influence individual susceptibility to radiation-induced cancer. In addition to the mutagenic effects of ROS and AFN, there is also evidence that oxidative stress plays a fundamental role in epigenetic modifications.
 Conclusion: As a result of radiation exposure, damage occurs that causes genetic and epigenetic changes, leading to changes in the level of protein expression due to changes in the methylation of cytosine residues in DNA, modification of histones, and regulation of microRNA expression.

CARCINOGENICITY OF IONIZING RADIATION:
 A LITERATURE REVIEW

Relevance: According to WHO, malignant neoplasms rank second in population mortality structure due to a constantly increasing influence of
 technogenic factors that have a direct carcinogenic effect on the body and suppress defense mechanisms. Ionizing radiation plays a special role in
 the development of cancer. It is used in industry, agriculture, medicine, and scientific research as a diagnostic tool in modern healthcare and radiation therapy for cancer treatment. The consequences of radiation influence are not only the result of a direct effect on the body but also a delayed
 one through generations of parents and grandparents. According to the radiobiological hypothesis, any level of radiation, no matter how small,
 ОБЗОРЫ ЛИТЕРАТУРЫ
 Онкология и Радиология Казахстана, №4 (70) 2023 45
 poses a risk of long-term consequences, including cancer, in exposed people and their descendants of the first two generations. That is, cancerous
 tumors are likely consequences of the influence of radiation. Despite various theories of the biological effect of low doses of ionizing radiation,
 most authors attach primary importance to DNA damage in the manifestation of genetic effects (the concept of non-threshold mutational action).
 The study aimed to highlight the role of ionizing radiation in tumorigenesis.
 Methods: Data from MEDLINE, Embase, Scopus, PubMed, Cochrane Central Register of Controlled Trials was analyzed to select and analyze
 relevant information over the past 10 years using the keywords: gamma irradiation, spontaneous oncogenesis, prevention of oncogenesis,
 Results: Radiation exposure may increase the risk of cancer development due to epigenetic changes leading to increased genomic instability
 (GI) and/or specific suppression of tumor suppressor genes. Changes in the TP53 gene network expression occur; the most significant genes as
 predictors of carcinogenesis are ST13, IER3, BRCAI, LRDD, and MRAS. Epigenetic changes also influence individual susceptibility to radiation-induced cancer. In addition to the mutagenic effects of ROS and AFN, there is also evidence that oxidative stress plays a fundamental role in
 epigenetic modifications.
 Conclusion: As a result of radiation exposure, damage occurs that causes genetic and epigenetic changes, leading to changes in the level of
 protein expression due to changes in the methylation of cytosine residues in DNA, modification of histones, and regulation of microRNA expression.

Exploring patients' experiences with telehealth in obstetrics care during the COVID-19 pandemic: A qualitative study.

The aim of this study was to evaluate patients' experiences with telehealth provision of obstetrics and gynecology care during the COVID-19 pandemic qualitatively. In this study, a qualitative research design, namely descriptive phenomenology, was employed. Participants were recruited from the OB department at UMass Memorial Medical center in Worcester, MA, between 6/2020 and 7/2020. Between June 2020 and July 2020, in-depth interviews were conducted with 18 women receiving care at the Obstetrics and Maternal and Fetal Medicine clinics. Data were analyzed using qualitative thematic analysis, as outlined by Braun and Clarke. Telehealth is a feasible and safe health-care tool that is available during these unprecedented times. This study provided qualitative evidence based on patients' perspectives and experiences. Participants' meanings in relation to their experiences of using telehealth services emerged from the data in four themes: the overall experience of using modern telehealth platforms, telehealth and its perceived benefits, telehealth and its perceived challenges, and telehealth and its potential future use. While this study highlights areas in telehealth implementation that require improvement, the overall positive experiences and consistent perceived benefits of most participants suggests that telehealth can be an important tool in healthcare delivery for appropriate patients and situations moving forward in a post-pandemic world. During the global pandemic, telehealth has been recognized to have the potential to play a critical role in healthcare delivery. Establishing qualitative evidence-based practices in the emerging field of telehealth for OB services is pivotal to mitigate potential safety, feasibility, and cost issues that could be associated with the rapid adoption of telehealth. Yet, this qualitative study However highlighted several challenges that are necessary to be addressed in order for telehealth to meet maximum effectiveness and functionality in the future.

Whale optimization approach for early heart disease prediction based on FCM using DCNN

Heart disease is the leading cause of death worldwide. It has an impact on not only the health of patients but also the economies and expenses of the countries. Numerous machine learning and data mining approaches are being developed and explored currently in order to predict various diseases. This paper aims to address the pressing global issue of heart disease by leveraging machine learning and data mining techniques. Specifically, it focuses on utilizing a Fuzzy C means (FCM) approach for attribute segmentation, employing the Whale Optimization Algorithm (WOA) for feature selection, and utilizing Deep Convolutional Neural Networks (DCNNs) for medical diagnosis and early prediction. In this study, the initial stage involves segmenting patient records' attributes using the FCM method. Subsequently, high-ranking features are selected through the WOA algorithm. These segmented features are then input into DCNNs to construct a robust medical diagnosis system and enable early-stage prediction. The DCNNs autonomously extract crucial features without human intervention, enhancing the accuracy of disease prediction. The performance evaluation of the proposed classifier is conducted using the Python platform, with the DCNN achieving an impressive accuracy level of 90.12% during testing. This indicates the DCNN's capability to accurately predict the presence or absence of cardiac disease, showcasing its potential as an effective tool in healthcare. The integration of FCM attribute segmentation, WOA feature selection, and DCNN-based prediction holds significant practical implications. It offers healthcare professionals a valuable tool for diagnosing and predicting heart disease early, potentially saving lives.

Weighing the Weight of Bedridden Patient by using Strain Gauge (Weighing Scale) - Prototype

In today’s healthcare landscape, weighing beds plays a pivotal role in patient care and management. These specialized beds provide critical data on a patient’s weight, which is integral for monitoring health, tailoring treatment plans, and ensuring accurate medication dosages in bed rest patients, etc. They are particularly crucial in critical care settings, where precise weight measurements are essential for fluid balance and medication adjustments. Weighing beds also has a significant impact on preventive healthcare, enabling timely interventions and lifestyle recommendations based on weight-related health risks. In post-operative care, they assist in assessing factors like fluid retention, and guiding post-surgical interventions. Furthermore, in pediatric healthcare, weighing beds are indispensable for monitoring growth and development. Integration with electronic health records streamlines data management, enhancing efficiency for healthcare providers. Overall, weighing beds are indispensable tool in modern healthcare, contributing to comprehensive patient care and well-being. As the medication requirement of the bedridden patient depends on weight, overdose or underdose of the drug is expected to complicate the problems further. Till now, in the market, there were available detecting devices, but the main problem with them is those devices lift the patient totally or lift the bed totally; For this workforce is more required and also time-consuming and cost-effective. For bedridden patients, it is difficult to measure body weight due to lifting and equipment, as well as the need for a workforce. Some beds have inbuilt measuring sensors, but they are cost-effective for bed purchase. For this, there is a possible way to measure the patient’s weight by using a strain gauge sensor

Measuring practice preference variation for quality improvement: development of the Neonatology Survey of Interdisciplinary Groups in Healthcare Tool (NSIGHT)

BackgroundUnderstanding behavioural psychology and the human side of change are guiding principles for quality improvement (QI). Tools to measure these to guide improvement efforts are lacking.MethodsWe created a clinical vignette-based...

Implementation of a digital tool for population management in Primary Health Care.

Describe the implementation of a digital diagnostic and territorial monitoring tool in primary healthcare. Quantitative and qualitative study, developed in 14 basic healthcare units in São Paulo, with community health workers, coordinators, nurses, and physicians. Data collection occurred in four phases: analysis of the instruments used by the team for territory management; development of the digital tool; training and implementation; and evaluation after 90 days using focus groups. Descriptive analyses were conducted by calculating absolute and relative frequencies to treat quantitative data. Qualitative data were subjected to content analysis. Three hundred thirty-four professionals participated in the study. In the first step, territory management's main challenges were filling out various instruments, system failures, data inconsistency, internet infrastructure/network, and lack of time. Therefore, a digital tool was developed consisting of 1) a spreadsheet recording the number of family members and markers of health conditions, date of visit, and number of return visits; 2) a spreadsheet with a summary of families visited, not visited, and refusals; and 3) a panel with a summary of the data generated instantly. In the evaluation, after the initial use of the tool, the themes that emerged were integration of the tool into daily work, evaluation of the digital tool implementation process, and improvement and opportunities for improvement. Faced with the challenges faced by family healthcare teams when filling out systems and managing the territory, the tool developed provided greater reliability and agility in data visualization, reduced the volume of instruments, and optimized the work process.

Developing trusted healthcare tools: How physiology can unlock the potential of artificial intelligence for health

Developing trusted healthcare tools: How physiology can unlock the potential of artificial intelligence for health

Impact of Machine Learning in Women\'s Health

Abstract: Machine learning has become a potent tool in healthcare, and study on its effects on women's health is becoming more and more essential. The current situation of women's health and healthcare inequities for women, particularly those from marginalized populations, are discussed in this study. It demonstrates how breast cancer detection, pregnancy problems prediction, and improved access to reproductive healthcare can all be done using machine learning to enhance women's health outcomes. The first section of the article discusses the current situation of women's health and healthcare inequities, including differences in the results of maternal healthcare for women of colour and restricted access to reproductive healthcare. The review of machine learning and its ability to correct these inequalities follows. In particular, it covers the application of machine learning to forecast pregnancy problems, such preterm birth and pre-eclampsia, which disproportionately impact women of colour.

Economic assessment of the cost of developing a voice assistant for oncology patients

Subject. Voice assistant is a developing digital healthcare tool that arouse great interest due to its ease of use. In the literature, there is insufficient data on its use in oncology, the cost of development, and experience in domestic platforms application. Objectives. The study aims to develop an approach to calculating the cost of creating a skill in the Alice voice assistant system for patients with skin cancer. Methods. The experience of creating a skill for patients with breast cancer is taken as a basis. We used the methodology for calculating the cost of software product under the calculation method by item. To estimate actual programming costs, we conducted a timekeeping experiment. The model contained 50 scripts with 50,000 lines of code. To evaluate the productivity of IT developers, we applied the volume of software texts, and the number of lines was taken as the number of operators. Results. The total cost of developing a skill for "Alice" is 2,097,190.76 RUB, including the salary fund of the core staff (61%), overhead costs (33.2%), electricity cost (0.2%), depreciation (0.2%). In the total share of labor cost of the core staff, programming costs prevail (61%). Conclusions. The approach to cost calculation for creating a skill for "Alice" is proposed for the first time. We simulated and predicted costs of developing a voice assistant for patients with skin cancer. The development of the skill scenario and the format of the dialog tree are critical and require creating a working group of specialists who contact patients at different stages. The development of the "Alice" skill using the Python can be classified as medium type of complexity, as model solutions and standard algorithms are used in the process.

Personalized medicine in a community health system: the NorthShore experience.

Genomic and personalized medicine implementation efforts have largely centered on specialty care in tertiary health systems. There are few examples of fully integrated care systems that span the healthcare continuum. In 2014, NorthShore University HealthSystem launched the Center for Personalized Medicine to catalyze the delivery of personalized medicine. Successful implementation required the development of a scalable family history collection tool, the Genetic and Wellness Assessment (GWA) and Breast Health Assessment (BHA) tools; integrated pharmacogenomics programming; educational programming; electronic medical record integration; and robust clinical decision support tools. To date, more than 225,000 patients have been screened for increased hereditary conditions, such as cancer risk, through these tools in primary care. More than 35,000 patients completed clinical genetic testing following GWA or BHA completion. An innovative program trained more than 100 primary care providers in genomic medicine, activated with clinical decision support and access to patient genetic counseling services and digital healthcare tools. The development of a novel bioinformatics platform (FLYPE) enabled the incorporation of genomics data into electronic medical records. To date, over 4,000 patients have been identified to have a pathogenic or likely pathogenic variant in a gene with medical management implications. Over 33,000 patients have clinical pharmacogenomics data incorporated into the electronic health record supported by clinical decision support tools. This manuscript describes the evolution, strategy, and successful multispecialty partnerships aligned with health system leadership that enabled the implementation of a comprehensive personalized medicine program with measurable patient outcomes through a genomics-enabled learning health system model that utilizes implementation science frameworks.