The following Digital Health Innovation Frequently Asked Questions (FAQ) serves as comprehensive guide outlining the opportunities, challenges, and practical considerations of implementing artificial intelligence and digital health technologies specifically within osteopathic medicine and healthcare, covering topics from basic AI concepts to ethical concerns, training requirements, and future policy directions.

Digital Health Innovation Community of Practice FAQ for Osteopathic Medicine

Last Updated: December 2025

Overview

Information provided through the AOiA Digital Health Innovation Community of Practice is for educational purposes only. Users must obtain approval from their healthcare institutions before implementing any technologies or processes in patient care or operations. All implementation decisions should align with local institutional policies and procedures

Disclaimer

Opportunities & Challenges

  • Artificial Intelligence, or AI, is a field of computer science where machines are designed to perform tasks that typically require human intelligence, such as recognizing patterns, learning from data, problem-solving, and making decisions. These systems aim to enhance human capabilities, supporting healthcare professionals with tasks that range from analyzing medical data to predicting patient outcomes.

    • Machine Learning (ML): A form of AI where systems learn from data to improve their performance over time. ML can help predict patient outcomes, customize treatment plans, and identify trends in patient health

    • Natural Language Processing (NLP): This technology enables AI to understand and process human language, which can be useful for analyzing clinical notes, improving patient communication, and streamlining documentation

    • Computer Vision: Used in medical imaging, computer vision can assist physicians by analyzing X-rays, MRIs, and other scans, potentially spotting issues more quickly or precisely than traditional methods.

    • Robotic Process Automation (RPA): This automates repetitive administrative tasks like data entry, billing, and appointment scheduling, freeing up time for osteopathic physicians to focus on direct patient care.

    • Agentic AI: A form of RPA whereby software "takes action" by completing tasks described by the User. Most new agents utilize a Large Language Model (see below) as both the interface with and the engine of agent software. Its greatest uses are for complicated workflows (e.g. a program that collects medical history from a patient) or tasks that require input of information as well as output (e.g. a program that finds lab values, inputs them into a calculator, and outputs a value like creatinine clearance). AI agents are increasingly being deployed in healthcare settings to automate complex care coordination tasks, including patient data retrieval, treatment plan creation, and care team communication. These agents can handle multistep processes that previously required significant human intervention, helping clinicians make more efficient decisions while maintaining oversight.

    • Predictive Analytics: This involves using data to forecast future health events, such as disease risks, helping to personalize preventive care for individual patients

    • Large Language Models (LLM): A form of predictive analytics that predicts the most acceptable response to input text (a prompt). Described by some as "auto complete with superpowers", its greatest uses are summarizing documents, classifying things into set categories, and pretending to create new things (text, picture, or video) by compiling finite but small copied parts in a way that meets an input description. LLMs have evolved significantly in 2025, with healthcare-specific models demonstrating improved accuracy in medical terminology, diagnostic suggestions, and patient communication. However, healthcare organizations must remain vigilant about model limitations, including potential hallucinations and the need for clinical validation.

    • Multimodal AI: An advanced form of AI that integrates and analyzes multiple types of data simultaneously—such as medical imaging, lab results, clinical notes, genomic data, and wearable device outputs—to provide a comprehensive view of patient health. Multimodal AI systems consistently outperform single-data-source approaches by 6-33% across various healthcare applications, including diagnosis, treatment planning, and outcome prediction. This technology enables more holistic clinical decision-making by combining diverse data streams in ways that mirror how experienced clinicians reason through complex cases.

    • Ambient Clinical Intelligence (ACI): AI-powered systems that listen to patient-clinician conversations during visits and automatically generate clinical documentation in real-time. These "AI scribes" reduce documentation burden, decrease after-hours charting time by up to 2.5 hours per week, and allow physicians to maintain better eye contact and engagement with patients. Major health systems including Kaiser Permanente, Cleveland Clinic, and University of Chicago Medicine have deployed ambient AI to over 4,000+ clinicians, with studies showing improved physician satisfaction, reduced burnout, and enhanced patient experience. Clinicians review and edit all AI-generated notes before they become part of the medical record.

  • The answer to this varies based on who you ask and the role of the provider, generally most believe that AI cannot replace healthcare providers. AI is designed to support, augment and enhance healthcare, not replace the vital roles that healthcare providers play.

    For example, AI cannot perform essential tasks related to patient examination and procedures (i.e., knee injections) without humans. In osteopathic medicine, AI can handle routine administrative tasks, analyze complex data, be a health scribe and assist with decision-making, allowing physicians to dedicate more time to hands-on, holistic patient care.

    By managing tasks that don't require human touch or clinical judgment, AI enables osteopathic practitioners to deepen their patient relationships and focus on treating the whole person and even to chart during hands on Osteopathic Manipulative Treatment (OMT) procedures, ensuring that technology enhances rather than overshadows the essential human elements of healthcare. Current evidence from 2025 reinforces that AI functions best as a collaborative tool rather than a replacement. Studies comparing ChatGPT to physician diagnoses show that while AI can perform well on diagnostic tasks, it lacks the contextual understanding, empathy, and hands-on clinical skills essential to patient care. The most successful implementations combine AI's analytical capabilities with physicians' clinical judgment, particularly in osteopathic medicine where the whole-person approach and manual medicine techniques remain fundamentally human-centered.

  • Current technology has enabled the engaged physician to offload non-clinical but important and burdensome tasks. Today, this could mean documenting medical care, completing insurance forms, searching the latest literature for medical answers, or writing effective letters to secure grants, obtain prior authorization, or resolve claim denial disputes.

    • Enhanced personalized care by automating routine tasks, allowing physicians to decrease desktop burden and focus on high-touch and high-tech, human-centered care

    • Increased health-promotive, holistic care with access to vital information and behavioral metrics in the home via wearable devices

    • Improved workflow efficiency and accessibility to advanced clinical knowledge

    • Advanced education and continuous learning

    • Increased capacity for data-driven decision making at training levels and elevate bedside patient care

    • Fostering innovation in osteopathic principles and practice

    • Assessing and sorting large volumes of clinical cases. For example, identifying patients with clinical characteristics most likely to benefit from a particular treatment, procedure, or OMM technique

    • Real-time clinical decision supportwith AI providing immediate access to evidence-based research and treatment guidelines at the point of care

    • Digital pathology advancement with AI-powered diagnostic tools achieving 29% higher cancer detection rates in screening, including 24% more early-stage tumors

    • Accelerated drug discovery with generative AI enabling development of novel medications in months rather than years, with predictions that over 30% of new drugs will be discovered using AI by 2025

    • Enhanced chronic disease management through continuous remote monitoring and AI-powered personalized interventions for conditions like diabetes, heart failure, and hypertension

    • Improved diagnostic accuracy in specialties such as radiology, dermatology, ophthalmology, pathology, and cardiology where AI can detect subtle abnormalities that may be missed by human review alone

    • Personalized care using predictive analytics specific to individual needs to optimize outcomes

    • Improved preventive care with real-time tracking, early intervention, and personalized recommendations, insights and patient education

    • Customized patient engagement through adaptive communication tools

    • AI-powered precision medicine that analyzes genetic data, lifestyle factors, and real-time health metrics from wearables to create highly individualized treatment plans

    • Predictive health modeling that can identify disease risks years before clinical symptoms appear, enabling earlier interventions for conditions like Alzheimer's, COPD, and kidney disease

    • Dynamic treatment adjustments based on continuous monitoring data, allowing providers to modify care plans in real-time as patient conditions change

    • High-volume, low-acuity tasks automated to free up clinical resources

    • Enhanced training efficiency through virtual education platforms

    • Optimized workforce scheduling and resource management for equitable care delivery

    • Ambient clinical documentation reducing documentation time by 30-40%, allowing providers to see more patients without increasing workload or burnout

    • AI-powered triage and patient routing systems that match patients with appropriate providers based on complexity, specialty needs, and availability

    • Virtual care assistants and chatbots handling routine patient inquiries, appointment scheduling, and medication reminders, freeing clinical staff for complex cases

    • Telehealth integration reaching sustainable equilibrium, with AI determining optimal scenarios for virtual versus in-person care, particularly valuable for behavioral health and oncology specialist consultations

    • Remote monitoring and telemedicine increase care access, especially for underserved populations

    • AI-powered tools assist in chronic disease management, wellness and prevention, helping patients stay engaged with their care plans

    • Improved outcomes through personalized treatment strategies and real-time feedback mechanisms

    • Enhanced patient-physician communication with ambient AI allowing doctors to maintain eye contact and full attention during visits rather than typing notes

    • Access to AI-powered personal health tracking through smart wearables that monitor activity, heart health, sleep patterns, and other vital signs with personalized coaching

    • Improved care coordination with multimodal AI systems integrating data from multiple sources to provide comprehensive treatment plans

    • Greater health equity through AI-enabled access to specialty consultations and advanced diagnostics in underserved areas via telemedicine platforms

Ethical Considerations & Patient Privacy

    • Protecting patient privacy through encryption and secure data storage

    • Maintaining empathy and the human touch despite remote patient care

    • Establishing mechanisms to identify biases, hallucinations, and confabulations, with recalibration systems where necessary

    • Maintaining experienced clinician oversight of automated processes to ensure the identification, remediation, and prevention of medical errors

    • Obtaining meaningful informed consent from patients for AI-assisted documentation and care, with clear explanation of how data is used, stored, and protected

    • Addressing algorithmic bias that may disproportionately affect certain patient populations based on race, ethnicity, socioeconomic status, or other demographic factors

    • Ensuring transparency in AI decision-making processes through explainable AI (XAI) techniques that allow clinicians to understand how recommendations are generated

    • Balancing the efficiency gains from AI with the preservation of the patient-clinician relationship and therapeutic alliance

    • Managing patient expectations about AI capabilities while preventing over-reliance on automated systems

    • Implement robust data protection policies, including encryption and secure storage

    • Provide education and transparency about data usage and protection practices

    • Ensure compliance with HIPAA, SOC2, HITRUST and GDPR to maintain legal standards and patient trust

    • Encourage use of digital health software that has earned the DiME Seal, which recognizes products demonstrating performance against comprehensive standards in evidence, usability, privacy, and security

    • Establish clear data governance frameworks that specify who has access to patient data, for what purposes, and for how long

    • Implement enhanced cybersecurity measures including firewalls, intrusion detection systems, and regular security audits to protect against increasing cyber threats

    • Ensure AI vendors and partners undergo rigorous security assessments and maintain appropriate certifications (SOC2, HITRUST)

    • Provide patients with transparency about AI usage through consent processes that explain recording, data processing, and retention policies for ambient documentation

    • Maintain patient control with opt-out options and the ability to review all AI-generated documentation in their medical records

  • Healthcare organizations must implement multi-layered quality assurance processes including:

    • Mandatory clinician review and approval of all AI-generated documentation before it enters the medical record

    • Continuous monitoring and evaluation of AI outputs for accuracy, completeness, and clinical appropriateness

    • Use of validated quality assessment instruments (such as PDQI-9) to systematically evaluate AI-generated clinical notes

    • Feedback loops that capture clinician corrections and patient concerns to improve AI performance over time

    • Limited deployment initially to specific use cases with highly repetitive scenarios before expanding to more complex clinical situations

    • Clear protocols for handling AI errors when detected, including incident reporting and root cause analysis

    • Regular retraining of AI models with diverse, representative datasets to minimize bias and improve accuracy across patient populations

Implementation & Training

    • Provide AI educational resource hub and training programs for students, staff, faculty, and administrators in Undergraduate Medical Education (UME) and Graduate Medical Education (GME) spaces

    • Provide digital health technology resources and expand networks for colleges of osteopathic medicine to collaborate and share technology resources and best practices

    • Foster collaborative innovation through partnerships via engagement with research and industry digital health leaders

    • Drive policy and advocacy efforts to support ethical and responsible AI use

    • Focus on practical implementation strategies for trainees to seamlessly integrate technology into clinical care

    • Purposeful exposure to and use of AI-enabled tools and digital health products in both school and post-graduate training programs

    • Establishment of dedicated AI and digital health centers within academic medical centers (such as the AMA's Center for Digital Health and AI launched in October 2025) to coordinate strategy, education, and implementation

    • Development of AI governance frameworks and responsible AI principles to guide safe and ethical deployment

    • Creation of chief AI officer positions within health systems to oversee AI integration across clinical operations

    • Pilot programs and proof-of-concept studies to evaluate AI tools before widespread deployment, including rigorous assessment of accuracy, usability, and impact on workflow

    • Quality improvement initiatives that track key performance indicators including documentation time reduction, clinician satisfaction, patient experience, and clinical accuracy

    • Develop standardized curricula integrating digital health technologies into existing medical education frameworks at all training levels

    • Create open-access digital platforms (or partner with existing vendors of same) for collaborative education and AI simulation tools

    • Create space to dialogue and elevate education paradigms toward co-creation with AI (e.g., chatbot advisors and tutoring systems)

    • Establish continuous real-time and personalized digital learning programs to ensure practitioners remain up to date with clinical practice guidelines

    • Create clinical practice areas at little or no cost to patients by adopting the lower implementation costs of digital healthcare's virtual clinics

    • Hands-on exposure to AI tools during clinical rotations, allowing students to practice using ambient documentation, diagnostic AI, and clinical decision support systems in supervised settings

    • Integration of AI competency milestones into accreditation standards, ensuring trainees develop appropriate skills in using, evaluating, and overseeing AI technologies

    • Virtual simulation environments where learners can practice complex procedures and diagnostic reasoning with AI-generated scenarios and feedback

    • AI-powered personalized learning platforms that adapt educational content to individual learner needs, knowledge gaps, and learning pace

    • Education on AI limitations, bias recognition, and responsible use, preparing future physicians to be informed consumers and deployers of AI technology

  • Healthcare has transitioned from an AI adoption laggard to a leader, with the industry now deploying AI at 2.2 times the rate of the broader economy. Key adoption statistics include:

    • 22% of healthcare organizations have implemented domain-specific AI tools, representing a 7-fold increase over 2024

    • Health systems lead up to adoption at 27%, followed by outpatient facilities at 18% and payers at 14%

    • Ambient clinical documentation has become one of the most widely adopted AI applications, with major health systems deploying to thousands of clinicians

    • AI is transitioning from pilot projects to production deployment, with proven use cases scaling across organizations

    However, challenges remain:

    • Only 5-10% of AI solutions demonstrate measurable, meaningful clinical impact

    • Some vendors are rebranding existing analytics tools as "AI" with minimal functional changes

    • Extensive due diligence by clinical, technical, and business experts is required to identify genuinely valuable AI solutions

    • Interoperability issues persist as organizations integrate multiple AI tools with existing EHR systems

Security & Resilience

    • Implement robust cybersecurity measures, including firewalls and intrusion detection

    • Provide comprehensive training for healthcare staff on data security best practices

    • Ensure compliance with HIPAA and GDPR for data protection

    • Stay informed about emerging threats and reference international frameworks such as SOC2, HITRUST, GDPR for best practices

    • In March 2025, HHS proposed significant modifications to the HIPAA Security Rule to require health plans, clearinghouses, and providers to strengthen cybersecurity protections for electronic protected health information (ePHI). These proposed updates represent the first major revision to security standards since their initial issuance.

    • Increased scrutiny of technology vendor partners to assess their vulnerability to attacks and data protection practices

    • Implementation of AI and machine learning tools specifically designed to detect abnormal network activity that could indicate a breach

    • Regular security audits and penetration testing to identify vulnerabilities before they can be exploited

    • Incident response planning with clear protocols for responding to breaches, including notification procedures and containment strategies

    • Zero-trust architecture approaches that verify every access request regardless of whether it originates inside or outside the network perimeter

Practice Management & Administrative Efficiency

    • Automate documentation using AI-powered natural language processing and optical character recognition at the bedside

    • Optimize patient scheduling with intelligent algorithms

    • Automate billing and coding processes to reduce administrative burdens

    • Enhance communication across care teams using digital collaboration tools

    • Utilize data analytics for practice management and quality assurance

    • Increase diagnostic speed and accuracy in fields such as radiology, ophthalmology, dermatology, pathology, psychiatry, neurology, and cardiology

    • Ensure clinical compliance with published and accepted standard of care

    • Help triage, sort, and route patients to the most appropriate tests, specialty referrals, and treatments

    • AI-powered appointment scheduling that analyzes patient EHR data to match them with the most appropriate provider based on complexity and specialty expertise

    • Automated prior authorization processes that use AI to compile necessary documentation and submit requests, reducing approval delays

    • Intelligent billing code recommendations generated by ambient AI during documentation, improving coding accuracy and reducing claim denials

    • AI-driven workflow optimization that analyzes practice patterns to identify bottlenecks and recommend efficiency improvements

    • Reduced "pajama time" (after-hours documentation) by 2.5 hours per week per clinician through ambient clinical intelligence

    • 30-40% improvement in overall workflow efficiency in organizations that have successfully implemented AI documentation tools

    • Faster diagnostic turnaround times with AI pre-screening of images and test results to flag urgent findings for immediate physician review

Future Directions & Policy

    • Drive policy changes to promote AI and digital health adoption in education and practice

    • Secure federal and state funding for AI research and development initiatives

    • Develop accessible, collaborative education frameworks in partnership with accrediting bodies at all levels of medical training and practice via technology-centric metrics and milestones

    • Innovate applications of existing use cases to current clinical workflow problems and openly share both successes and failures

    • Advocate for favorable regulatory frameworks that enable responsible AI innovation while maintaining patient safety standards

    • Develop clear guidance on AI-related reimbursement policies, including coverage for AI-assisted care delivery and remote monitoring services

    • Establish accreditation standards that incorporate digital health and AI competencies into medical education requirements

    • Create pathways for FDA approval of AI clinical decision support tools that balance safety with innovation speed

    • Support research funding specifically for studies evaluating AI effectiveness, safety, and equity in real-world clinical settings

    • Promote data sharing initiatives and interoperability standards that enable AI development while protecting patient privacy

    • Address liability and malpractice considerations for AI-assisted clinical decision-making

  • Several cutting-edge developments are on the horizon:

    • Digital Twins: AI-powered virtual representations of individual patients that integrate continuous data from wearables, EHRs, genomics, and other sources to simulate disease progression and predict treatment responses

    • AI Agents for Care Coordination: Autonomous AI systems that can complete complex multi-step tasks like reviewing patient records, scheduling appointments, ordering appropriate tests, and communicating with care teams—all with appropriate human oversight

    • Generative AI for Drug Discovery: Tools that can design novel molecular structures and predict their therapeutic potential, dramatically accelerating the development of new treatments

    • Computer Vision for 3D Medical Imaging: Advanced AI systems that can analyze complex 3D datasets from MRI and CT scans, providing comprehensive views of anatomical structures and detecting abnormalities that may be missed in 2D analysis

    • AI-Powered Clinical Protocol Design: Systems that help pharmaceutical companies optimize clinical trial designs and participant selection, reducing time to market for new therapies

    • Voice-First Interfaces: Natural language AI assistants that allow clinicians to interact with EHRs and order entry systems using voice commands, further reducing desktop burden

    • Federated Learning: Techniques that allow AI models to learn from data across multiple institutions without sharing raw patient data, enabling more robust AI while protecting privacy

Conclusion

As we advance into 2025 and beyond, AI and digital health technologies offer unprecedented opportunities to enhance osteopathic medical practice while preserving its core principles of holistic, patient-centered care. The key to success lies in thoughtful implementation that prioritizes patient safety and clinical accuracy, maintains the essential human connection, provides adequate training, addresses ethical concerns, continuously evaluates outcomes, and ensures that technology serves as a tool to enhance—not replace—clinical judgment.

The rapid pace of AI advancement requires ongoing education, critical evaluation, and adaptive policies. Organizations that approach AI with both enthusiasm and appropriate skepticism, implementing robust quality assurance processes and maintaining strong clinician oversight, will be best positioned to realize AI's benefits while mitigating its risks. Osteopathic medicine's emphasis on treating the whole person positions the profession uniquely to lead in demonstrating how advanced technology can be integrated thoughtfully with humanistic, relationship-based care.

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