Personal Health Records Research Articles

Abstract MP56: Screening for Familial Hypercholesterolemia: School Based Programs May Be Best Alternative

Introduction: Familial Hypercholesterolemia (FH) is an autosomal dominant condition characterized by elevated cholesterol and premature development of cardiovascular (CV) disease affecting 1/200 - 1/500 individuals. Only 10% of the affected population in the United States have been identified and treated compared to 50-70% in Scandinavian countries identified through school-based or universal screening programs. Hypothesis Statement: School based programs are the best mechanism for FH screening. Methods: The Healthy Hearts and Minds Program (HHM) through UCHealth performs biometric screenings on 5th, 7th, and 10th grade students in Northern Colorado. HHM tested 10,991 5th grade students (9/2017 - 5/2022) and identified 424 students with total cholesterol levels ≥200 mg/dL (range 200-302). A buccal smear test (APOB, LDLR, PCSK9) was performed on 99 individuals with the highest total cholesterol levels to identify genetic status. A binomial logistic regression model was used for analysis. Results: Of the 99 students, six tested positive (50% male, 10.3 years, 22.8 BMI) for FH with five (40% male, 11.3 years, 17.3 BMI.) reporting a variant of indeterminate significance. A sigmoidal association between total cholesterol and predicted chance of FH was identified with the inflection point at ~270 mg/dL. Conclusions: Comprehensive school based programs are the most effective and equitable means of identifying this genetic based disease and should be supported in a collaborative manner with health care systems, industry and the community. With availability to all students, diversity, equity, and inclusion concerns are addressed and provide at risk populations with personalized health information. The results from this study suggest a school-based screening program can be effective at identifying students most at risk for FH. These results further define the relation between total cholesterol and predicted chance of FH for a school-aged population where 245 mg/dL indicates a 10.3% of testing positive for FH whereas 275 mg/dL has a 54.8% chance. Additionally, the multiplier effect documented through cascade FH testing enhances the benefit of the information to other family members, as the autosomal dominant pattern insures that 50% of members will be at risk. The individual benefit of identifying FH early is the treatability of this condition. Early use of lipid lowering agents can make the CV disease risk equal to the general population. If left untreated, CV disease occurs approximately 10 years earlier which results in increased mortality and disability, along with increased health care costs. Therefore, if elevated cholesterol is present in school children, genetic testing is warranted and treatment considered by age 8-10 years, especially if signs of abnormal vascular plaquing is identified.

Construction of a comprehensive fetal monitoring database for the study of perinatal hypoxic ischemic encephalopathy

This article describes the methods used to build a large-scale database of more than 250,000 electronic fetal monitoring (EFM) records linked to a comprehensive set of clinical information about the infant, the mother, the pregnancy, labor, and outcome. The database can be used to investigate how birth outcome is related to clinical and EFM features. The main steps involved in building the database were: (1) Acquiring the raw EFM recording and clinical records for each birth. (2) Assigning each birth to an objectively defined outcome class that included normal, acidosis, and hypoxic-ischemic encephalopathy. (3) Removing all personal health information from the EFM recordings and clinical records. (4) Preprocessing the deidentified EFM records to eliminate duplicates, reformat the signals, combine signals from different sensors, and bridge gaps to generate signals in a format that can be readily analyzed. (5) Post-processing the repaired EFM recordings to extract key features of the fetal heart rate, uterine activity, and their relations. (6) Populating a database that links the clinical information, EFM records, and EFM features to support easy querying and retrieval.•A multi-step process is required to build a comprehensive database linking electronic temporal fetal monitoring signals to a comprehensive set of clinical information about the infant, the mother, the pregnancy, labor, and outcome.•The current database documents more than 250,000 births including almost 4,000 acidosis and 400 HIE cases. This represents more than 80% of the births that occurred in 15 Northern California Kaiser Permanente Hospitals between 2011–2019. This is a valuable resource for studying the factors predictive of outcome.•The signal processing code and schemas for the database are freely available. The database will not be permitted to leave Kaiser firewalls, but a process is in place to allow interested investigators to access it.

Empowering Chronic Kidney Disease Patients: A Personalized Electronic Health Record System for Enhanced Care and Management

Introduction: Electronic health records are critical for diagnosis and treatment, but their development can be challenging and time-consuming process. Personal health records provide a practical and accessible solution by empowering patients to take an active role in their care. This study presents a personalized electronic health record system for chronic kidney disease patients, highlighting its potential benefits on patient care.Material and Methods: In 2021, a practical study was conducted to develop and evaluate a personal health records software specifically for patients with chronic kidney disease. The analysis phase involved a literature review and validation process to review the dataset and key software functions. The study primarily focuses on the implementation process and exploratory usability evaluation results of the software. The development and evaluation process involved a health information management specialist and a medical informatics expert, and the software was evaluated by health information technology students using the Nielsen usability evaluation checklist.Results: In the evaluation of the produced software, 12 issues were identified, of which three were related to the principle of user freedom and mastery over the system, three were related to the principle of consistency and standards, one was related to the principle of aiding users in diagnosis, and five were related to error prevention.Conclusion: This study emphasizes the importance of compliance with clinical standards and user experience in developing successful personal health record software. Previous failures in similar systems have been attributed to non-compliance and unmet user expectations. The study aims to identify obstacles and improve the personal health records production process.

Decreasing Opioid Addiction and Diversion Using Behavioral Economics Applied Through a Digital Engagement Solution: Protocol for a Randomized Controlled Trial.

Despite strong and growing interest in ending the ongoing opioid health crisis, there has been limited success in reducing the prevalence of opioid addiction and the number of deaths associated with opioid overdoses. Further, 1 explanation for this is that existing interventions target those who are opiate-dependent but do not prevent opioid-naïve patients from becoming addicted. Leveraging behavioral economics at the patient level could help patients successfully use, discontinue, and dispose of their opioid medications in an acute pain setting. The primary goal of this project is to evaluate the effect of the 3 versions of the Opioid Management for You (OPY) tool on measures of opioid use relative to the standard of care by leveraging a pragmatic randomized controlled trial (RCT). A team of researchers from the Center for Learning Health System Sciences (CLHSS) at the University of Minnesota partnered with M Health Fairview to design, build, and test the 3 versions of the OPY tool: social influence, precommitment, and testimonial version. The tool is being built using the Epic Care Companion (Epic Inc) platform and interacts with the patient through their existing MyChart (Epic Systems Corporation) personal health record account, and Epic patient portal, accessed through a phone app or the MyChart website. We have demonstrated feasibility with pilot data of the social influence version of the OPY app by targeting our pilot to a specific cohort of patients undergoing upper-extremity procedures. This study will use a group sequential RCT design to test the impact of this important health system initiative. Patients who meet OPY inclusion criteria will be stratified into low, intermediate, and high risk of opiate use based on their type of surgery. This study is being funded and supported by the CLHSS Rapid Prospective Evaluation and Digital Technology Innovation Programs, and M Health Fairview. Support and coordination provided by CLHSS include the structure of engagement, survey development, data collection, statistical analysis, and dissemination. The project was initially started in August 2022. The pilot was launched in February 2023 and is still running, with the data last counted in August 2023. The actual RCT is planned to start by early 2024. Through this RCT, we will test our hypothesis that patient opioid use and diverted prescription opioid availability can both be improved by information delivery applied through a behavioral economics lens via sending nudges directly to the opioid users through their personal health record. ClinicalTrials.gov NCT06124079; https://clinicaltrials.gov/study/NCT06124079. PRR1-10.2196/52882.

The Use of Digital Personal Health Records (PHR) in Occupational Health.

The exact definition of a personal health record (PHR) is not clear, but it is generally defined as an electronic record of an individual's medical examination results and medication history that can be accurately tracked by the individual and family members. There are many opportunities to obtain personal PHR data in the field of occupational health, and the amount of information in a worker's PHR is enormous. It is necessary in this field to comprehensively integrate and accumulate not only information from medical examinations, but also records of the work environment and work records as personal health information.

Examining the Generalizability of Pretrained De-identification Transformer Models on Narrative Nursing Notes.

Narrative nursing notes are a valuable resource in informatics research with unique predictive signals about patient care. The open sharing of these data, however, is appropriately constrained by rigorous regulations set by the Health Insurance Portability and Accountability Act (HIPAA) for the protection of privacy. Several models have been developed and evaluated on the open-source i2b2 dataset. A focus on the generalizability of these models with respect to nursing notes remains understudied. The study aims to understand the generalizability of pretrained transformer models and investigate the variability of personal protected health information (PHI) distribution patterns between discharge summaries and nursing notes with a goal to inform the future design for model evaluation schema. Two pretrained transformer models (RoBERTa, ClinicalBERT) fine-tuned on i2b2 2014 discharge summaries were evaluated on our data inpatient nursing notes and compared with the baseline performance. Statistical testing was deployed to assess differences in PHI distribution across discharge summaries and nursing notes. RoBERTa achieved the optimal performance when tested on an external source of data, with an F1 score of 0.887 across PHI categories and 0.932 in the PHI binary task. Overall, discharge summaries contained a higher number of PHI instances and categories of PHI compared with inpatient nursing notes. The study investigated the applicability of two pretrained transformers on inpatient nursing notes and examined the distinctions between nursing notes and discharge summaries concerning the utilization of personal PHI. Discharge summaries presented a greater quantity of PHI instances and types when compared with narrative nursing notes, but narrative nursing notes exhibited more diversity in the types of PHI present, with some pertaining to patient's personal life. The insights obtained from the research help improve the design and selection of algorithms, as well as contribute to the development of suitable performance thresholds for PHI.

RAMA: a risk assessment solution for healthcare organizations

Recent cyber-attacks targeting healthcare organizations underscore the growing prevalence of the sector as a prime target for malicious activities. As healthcare systems manage and store sensitive personal health information, the imperative for robust cyber security and privacy protocols becomes increasingly evident. Consequently, healthcare institutions are compelled to actively address the intricate cyber security risks inherent in their digital ecosystems. In response, we present RAMA, a risk assessment solution designed to evaluate the security status of cyber systems within critical domain, such as the healthcare one. By leveraging RAMA, both local stakeholders, such as the hospital’s IT personnel, and global actors, including external parties, can assess their organization’s cyber risk profile. Notably, RAMA goes beyond risk quantification; it facilitates a comparative analysis by enabling organizations to measure their performance against average aggregated mean scores, fostering a culture of continuous improvement in cyber security practices. The practical efficacy of RAMA is demonstrated through its deployment across four real-world healthcare IT infrastructures. This study not only underscores the significance of addressing cyber security risks within healthcare but also highlights the value of innovative solutions like RAMA in safeguarding sensitive health information and enhancing the sector’s overall cyber resilience.

Blockchain-secure patient Digital Twin in healthcare using smart contracts.

Modern healthcare has a sharp focus on data aggregation and processing technologies. Consequently, from a data perspective, a patient may be regarded as a timestamped list of medical conditions and their corresponding corrective interventions. Technologies to securely aggregate and access data for individual patients in the quest for precision medicine have led to the adoption of Digital Twins in healthcare. Digital Twins are used in manufacturing and engineering to produce digital models of physical objects that capture the essence of device operation to enable and drive optimization. Thus, a patient's Digital Twin can significantly improve health data sharing. However, creating the Digital Twin from multiple data sources, such as the patient's electronic medical records (EMR) and personal health records (PHR) from wearable devices, presents some risks to the security of the model and the patient. The constituent data for the Digital Twin should be accessible only with permission from relevant entities and thus requires authentication, privacy, and provable provenance. This paper proposes a blockchain-secure patient Digital Twin that relies on smart contracts to automate the updating and communication processes that maintain the Digital Twin. The smart contracts govern the response the Digital Twin provides when queried, based on policies created for each patient. We highlight four research points: access control, interaction, privacy, and security of the Digital Twin and we evaluate the Digital Twin in terms of latency in the network, smart contract execution times, and data storage costs.

Pemanfaatan Resume Medis Sebagai Portofolio Kesehatan Keluarga

Indonesia has entered to the digital health transformation era. One of the transformation is the obligation of health care facilities to organize medical record in electronic form. Peduli Lindungi application, which was originally only for Covid-19 tests and vaccines has now expanded is function as a Health Portfolio or Personal Health Record (PHR) in the Satu Sehat application. The Health Portfolio can be compiled based on the Medical Resume obatained from Health services. The use of medical resume for preparing health portfolio has not been widelu used because many people do not understand the function of medical resume. The aim of this activity is to explain the importance of a complete medical resume so that it can be used as material for preparing a family health portfolio. The method is the online seminar method. The result is a medical resume containing the identity and history of the patient’s illness after receiving health services. Medical resume can be used to build family health portfolio. The conclusion is that a family health portfolio needs to be prepared to help manage diseases that may arise in the future.

Exploring the Impact of Artificial Intelligence on Patient Care: A Comprehensive Review of Healthcare Advancements

Artificial Intelligence (AI) is revolutionizing healthcare by transforming disease identification, treatment, and management. Healthcare organizations are rapidly adopting AI technologies to improve patient outcomes, streamline operations, and optimize costs. Utilizing a broad toolkit comprising Robotics, Computer Vision, Natural Language Processing, and Machine Learning, AI has made significant advancements across various healthcare domains. AI-driven diagnostic systems are showcased for their precision in analyzing medical images, enabling early detection of diseases such as cancer. Personalized treatment plans and preventive treatments are made possible by predictive analytics, which uses large amounts of patient data to predict the course of a disease and identify those who are at risk. This leads to an improvement in patient care. Beyond clinical applications, AI is reshaping healthcare delivery through solutions like telemedicine, virtual consultations, and remote monitoring. Virtual Health Assistants, empowered by AI, deliver personalized health information, medication reminders, and lifestyle guidance, enhancing patient engagement and treatment adherence. Telemedicine systems employ Artificial Intelligence (AI) algorithms to enhance resource allocation, expedite appointment scheduling, and supply superior medical services to remote and isolated populations. Hence, AI’s potential to improve productivity, encourage creativity, and solve difficult problems through sophisticated data analysis and automation is what has made it so important in revolutionizing many sectors.

‘It’s all about connecting’: using visual methods to surface the multisensory and more-than-human dimensions of health information

Human bodies and health states are becoming increasingly digitised and datafied through the use of digital technologies such as mobile and wearable devices, apps and software, electronic medical records and social media platforms. The ways that people learn about their bodies and health through their encounters with other people, living things and elements of the natural environment are often invisible in portrayals of digital health technologies. This article reports on a project in which we sought to surface more-than-digital, multisensory, affective and more-than-human modes of learning and knowing about health and wellbeing. This project involved a series of qualitative online workshops which incorporated two visually based activities: participants’ creation of maps of personal health information ecologies, and their responses to photographic prompts showing aspects of natural environments. These maps and images inspired participants to express and discuss their thoughts and feelings related to the ways they learn about and conceptualise their health and wellbeing from and with a diverse set of human and nonhuman agents. Taking this approach, we were able to develop an understanding of digital health technologies and health literacies within the more-than-digital contexts and complex health information ecologies of people’s everyday lives.

Improved Medication Adherence of an Elderly Diabetic Patient at a Dwelling Home Using a Pill Dispenser and Personal Health Records.

Fookkun® is a pill dispenser in which single doses of several medications intended to be taken simultaneously are sealed in single film bags rolled onto a medication rotating drum. The system makes musical alert sounds when it is time for the patient to take the medications. If the patient misses a dose, a designated contact, such as the patient's child, is alerted. We conducted an experiment monitoring the use of a pill dispenser (Fookkun®) by an older patient. The participant was a 71-year-old woman with diabetes living in a dwelling home. The experiment lasted approximately 6 months. Fookkun® and the prototype data transmitter were installed at the patient's home. Fookkun®'s medication history data are displayed on the electronic medication record book (E-MRB) and the patient's pharmacist checks the patient's medication history on the E-MRB. The Fookkun® was effective in facilitating medication adherence. The pharmacist and the patient's daughter did not need to check the E-MRB because Fookkun® alerted them when the patient missed her medication. We believe that if the medication history data linked between a pill dispenser and an E-MRB can be shared among medical staff, this will contribute to a medical digital transformation in Japan in the future.

Evaluating Ontology-Based PD Monitoring and Alerting in Personal Health Knowledge Graphs and Graph Neural Networks

In the realm of Parkinson’s Disease (PD) research, the integration of wearable sensor data with personal health records (PHR) has emerged as a pivotal avenue for patient alerting and monitoring. This study delves into the complex domain of PD patient care, with a specific emphasis on harnessing the potential of wearable sensors to capture, represent and semantically analyze crucial movement data and knowledge. The primary objective is to enhance the assessment of PD patients by establishing a robust foundation for personalized health insights through the development of Personal Health Knowledge Graphs (PHKGs) and the employment of personal health Graph Neural Networks (PHGNNs) that utilize PHKGs. The objective is to formalize the representation of related integrated data, unified sensor and PHR data in higher levels of abstraction, i.e., in a PHKG, to facilitate interoperability and support rule-based high-level event recognition such as patient’s missing dose or falling. This paper, extending our previous related work, presents the Wear4PDmove ontology in detail and evaluates the ontology within the development of an experimental PHKG. Furthermore, this paper focuses on the integration and evaluation of PHKG within the implementation of a Graph Neural Network (GNN). This work emphasizes the importance of integrating PD-related data for monitoring and alerting patients with appropriate notifications. These notifications offer health experts precise and timely information for the continuous evaluation of personal health-related events, ultimately contributing to enhanced patient care and well-informed medical decision-making. Finally, the paper concludes by proposing a novel approach for integrating personal health KGs and GNNs for PD monitoring and alerting solutions.

Cohort profile: rationale and design of the Resource Center for Health Science (RECHS) project – a study of health hazards and medical cost burden among the Japanese population

PurposeThe increased global burden of non-communicable diseases and mental disorders is an urgent health challenge for countries around the entire world, especially those experiencing super-ageing societies, where over 21% of...

Association between fitness technology use and physical activity mediated by communication behaviors on social media.

Fitness technologies, such as smartphone applications and wearable tracking devices, have gained widespread popularity. This study had two main objectives: 1) to examine whether fitness technology use is associated with increased physical activity (PA) levels and 2) to investigate whether communication behaviors on social media mediated the association between fitness technology use and PA. Data were from the U.S. Health Information National Trends Survey 2022 (N = 6,252, weighted N = 258,418,467). Weighted linear regressions were conducted to examine the associations between fitness technology usage, physical activities, and communication behaviors on social media. Mediations were tested using PROCESS macro, a path-analysis based tool. Controlling for demographic and other known influences on PA, the findings revealed that users of fitness technology reported higher levels of both moderate PA (β = .41, p < 0.001) and strength training (β = .29, p < 0.001). Additionally, communication behaviors (i.e., sharing personal health information on social media and watching health-related videos on social media) mediated the relationship between fitness technology use and frequencies of strength training. The results underscored the potential of fitness technologies in enhancing PA levels through promoting communication behaviors on social media.

Leveraging Django and Machine Learning to Craft a Health Forecasting System, Tailoring Personalized Wellness Strategies

Abstract: The goal of this thesis is to develop a health forecasting system that combines Django with cutting-edge machine learning in order to customize wellness plans according to personal health information. The study addresses model selection, data preprocessing, and user interface creation as it investigates the potential synergies between machine learning and web development. Predictive health analytics potential and gaps are identified through a review of the literature. The goal of the project is to create a real-time, scalable health forecasting system while protecting user privacy. Neural networks and decision trees are two examples of machine learning methods that are utilized. The efficacy of the system in delivering precise health projections and augmenting user involvement in proactive wellness management is evaluated through empirical assessment and case studies. In order to further personalized healthcare solutions, practitioners, researchers, and developers can benefit greatly from the research's insightful contributions.

Social structural factors influencing personal health information disclosure

As a general behavior in civil social life, how can personal health information disclosure be affected by social structural factors when using the health application? This study used the Social Capital Theory to examine the personal health information behavior. Nationally representative cross-sectional survey data generated by the China Family Panel Studies(CFPS) in 2020 was utilized(N = 8628). Logistic regression analysis was employed to analyze the data. After controlling for age, gender, and education covariates, social interaction had no significant impact on health APP information disclosure behavior (OR=0.988),there is a significant positive relationship between trust(OR=1.282)/ reciprocity perception(OR=1.134) and health APP information disclosure behavior. More relational social capitals can enhance individual social trust, maintain reciprocity perception, maintain the balance and stability of social exchange relations.This study extends the application of social capital theory in the study of personal health information disclosure, providing theoretical support for the democratization process of social health governance.

How to Design and Evaluate mHealth Apps? A Case Study of a Mobile Personal Health Record App

The rapid growth of the mHealth market has led to the development of several tools to evaluate user experience. However, there is a lack of universal tools specifically designed for this emerging technology. This study was conducted with the aim of developing and verifying a user experience evaluation scale for mHealth apps based on factors proposed in previous research. The initial draft of the tool was created following a comprehensive review of existing questionnaires related to mHealth app evaluation. The validity of this scale was then tested through exploratory and confirmatory factor analysis. The results of the factor analysis led to the derivation of 16 items, which were conceptually mapped to five factors: ease of use and satisfaction, information architecture, usefulness, ease of information, and aesthetics. A case study was also conducted to improve mHealth apps concerning personal health records using this scale. In conclusion, the developed user experience evaluation scale for mHealth apps can provide comprehensive user feedback and contribute to the improvement of these apps.

The Use of Online Medical Record Functionalities in Older Adulthood: The Role of Use Encouragement and Access Frequency

This study aimed at predicting the use of online medical record functionalities in later life. Frequency of accessing the record and the encouragement to use it by healthcare provider served as the main predictors. The study’s theoretical framework was comprised of the facilitating conditions and the technology familiarity approaches . The data were obtained from the Health Information National Trends Survey and analyzed using logistic regression models. The sample included Internet users aged 60 years or older who accessed their online medical records at least once during the year prior to the survey (N = 623). The results show an interaction between access frequency and use encouragement concerning the use of online medical record for looking up test results and securely messaging healthcare providers. In both cases, the effect of the use encouragement was more pronounced in frequent than in non-frequent users. Access frequency and use encouragement were each positively associated with using online medical record for downloading personal health information. The results suggest that both studied factors are important for understanding online medical record use in older adulthood, therefore supporting each theoretical approach employed. In addition, the results underscore a differential role the use encouragement plays for older frequent and non-frequent online medical record users.

The feasibility and usability of a personal health record for patients with multiple sclerosis: a 2-year evaluation study.

Multiple sclerosis (MS) is a persistent inflammatory condition impacting the brain and spinal cord, affecting globally approximately 2.8 million individuals. Effective self-management plays a crucial role in the treatment of chronic diseases, including MS, significantly influencing health outcomes. A personal health record (PHR) is a promising tool to support self-management, potentially empowering patients and enhancing their engagement in treatment and health. Despite these promising aspects, challenges in implementation persist and PHRs are still a relatively new concept undergoing rapid development. This study aimed to assess the feasibility and usability of the PHR. Secondary objectives included evaluating implementation determinants, and exploring preliminary effects on quality of care for both patients and healthcare professionals (HCPs), self-management, self-efficacy for patients, job satisfaction, efficiency, and demand for HCPs, and preliminary effects on costs and health-related quality of life. This study had a mixed-methods design. Quantitative data of patients (n = 80) and HCPs (n = 12) were collected via self-reported questionnaires at baseline (T0), after one year (T1), and after two years (T2). One focus group interview was conducted at T2 with patients (n = 7), and another one with HCPs (n = 4), to get a more in-depth understanding of the feasibility and usability of the PHR via the Unified Theory of Acceptance and Use of Technology framework, and to further explore the secondary objectives in-depth. Most patients never logged in during the first year and logged in a couple of times per year during the second year, averaging around 15 min per log-in session. The HCPs mainly logged in a couple of times per year over the two years with an average use of six minutes per session. Patient usability and satisfaction scores were below average and moderate, respectively: with SUS-scores of 59.9 (SD = 14.2, n = 33) at T1 and 59.0 (SD = 16.3, n = 37) at T2, and CSQ-8 scores of 21.4 (SD = 5.0, n = 34) at T1, and 22.1 (SD = 5.0, n = 39) at T2. HCPs had similar usability and satisfaction scores. Multiple facilitators and barriers were identified by both patients and HCPs, such as (in)sufficient knowledge of how to use the PHR, lack of staff capacity and ICT obstacles. No significant differences were found in the preliminary effects. Qualitative data showed, among others, that both patients and HCPs saw the benefit of the PHR in terms of performance expectancy, by gaining more insight into health and health data, but challenges remained regarding effort expectancy, such as log-in issues and experiencing difficulties with information retrieval. The feasibility and usability were considered moderate by patients and HCPs; however, potential regarding the performance of the PHR was observed. Implementation challenges, such as the complexity of usage, lowered the adoption of the PHR. The evolving nature of PHRs requires ongoing evaluation and adaptation to optimize their potential benefits. Utilizing a participatory design approach and a dedicated implementation team could help in achieving this optimization, ultimately enhancing their adoption.