Applying AI to Successfully Assist in Digitizing the Healthcare Experience

Artificial intelligence (AI) clearly has great potential to transform healthcare delivery and improve patient outcomes. However, successfully applying AI requires a strategic, carefully planned approach that considers clinician workflows, patient impacts, and return on investment. With the right strategy, well-thought-through planning and effective implementation, AI can digitize and enhance many aspects of the healthcare experience while avoiding unintended consequences.

Streamlining Administrative Tasks with AI

One major opportunity for AI is automating administrative tasks to reduce burdens on clinicians and free up their time for direct patient care. Electronic health records (EHRs) powered by AI show promise in this area through features like:

· Automated documentation using natural language processing (NLP) to extract data from clinical notes and pre-populate templates for discharge summaries, referrals, and other documentation. Some applications claim to reduce documentation time by over 50%.

· Prior authorization assistance using NLP to extract details from requests, records, and policies to determine medical necessity and route requests appropriately. This can consume 15–25% of clinician time according to studies.

· Automated billing using AI to integrate documentation, orders, and services with insurance billing systems. By handling repetitive paperwork, AI aims to save clinicians substantial time spent on non-clinical tasks.

However, successfully automating administrative workflows requires addressing challenges like integrating with legacy systems, overcoming data silos, and ensuring AI recommendations can be easily understood and overridden by clinicians. Early pilot testing focused on usability is also important to build trust in AI tools and ensure they optimally fit into clinical routines.

Enhancing Medical Imaging Analysis with AI

When applied carefully to medical imaging, AI shows promise assisting radiologists. Deep learning models trained on huge annotated datasets can help detect anomalies, predict disease severity, and monitor treatment responses over time. By providing a “second opinion,” AI aims to improve diagnostic accuracy without replacing human judgment.

Some applications of AI to medical imaging include:

· Computer-aided diagnosis to highlight potential findings radiologists may miss on initial reads. Studies show AI can help detect more cancers, which may lead to earlier treatment and better outcomes.

· Automated measurements using AI to perform time-consuming manual tasks like lesion size calculations or volumetric analyses of lung scans. This standardizes assessments and frees up clinician time.

· Predicting disease progression by training models on longitudinal data to forecast how abnormalities may change over time. This could help optimize long-term treatment planning and management.

However, medical imaging also presents challenges for AI like differences between scanner manufacturers, image quality issues, and limited annotated training data for rare conditions. Extensive validation is needed before high-risk clinical use to ensure AI augments — not replaces — radiologist expertise. Usability must also be optimized to gain provider acceptance.

Powering Precision Medicine with AI

By combining genomic data with comprehensive EHRs, AI shows promise, powering precision medicine through personalized predictions, recommendations, and matches to clinical trials. Some applications include:

· Disease risk modeling using AI to analyze individual patient profiles and predict risks for complex chronic conditions based on unique genetic and clinical factors. This enables more customized prevention and treatment planning.

· Treatment selection assisting clinicians by using a patient’s medical history and genetic results to recommend the most appropriate drug therapies or procedures based on similar prior cases.

· Clinical trial matching applying AI to efficiently scan trials and match patients to those with eligibility criteria closely aligned to their specific medical attributes. This advances research participation.

Realizing AI’s potential for precision medicine requires overcoming challenges like integrating diverse data sources, ensuring model transparency, addressing privacy/security concerns, and demonstrating improved outcomes versus usual care. Early pilot testing and validation in clinical practice will also be important to gain provider trust in AI-driven recommendations.

Fitting AI into Clinical Workflows

For AI to benefit healthcare, solutions must optimally fit into existing clinician workflows and systems. Designing for usability from the start is crucial to avoid disrupting care processes or overburdening busy providers. Some best practices include:

· User-centered design incorporating clinician feedback into development to understand pain points AI could alleviate and ensure intuitive, non-disruptive interfaces.

· Integration with EHRs and other clinical systems so AI insights seamlessly appear where needed without extra steps or logins. Silos inhibit adoption.

· Explainability ensuring models provide transparency into how they derive recommendations so clinicians understand and trust AI guidance.

· Flexibility accommodating variations in specialty, department, or facility workflows through configurable settings that let AI deployments adapt as needed.

· Augmentation, not automation designing AI as a decision support tool clinicians can easily accept or reject rather than a replacement for human judgment.

Thoughtful design focused on the clinician experience from the start helps AI solutions optimally fit into complex clinical routines versus becoming disruptive burdens. This acceptance drives broader utilization and realization of AI’s benefits.

Demonstrating Return on Investment

For healthcare organizations to invest in AI, a clear return on investment (ROI) must be demonstrated. While AI promises long-term savings, upfront costs are high. Some strategies for showing ROI include:

· Early savings proofs focusing pilot programs in areas with quantifiable impacts like reduced documentation time or improved diagnostic accuracy to lower costs.

· Outcome improvements tracking whether AI helps achieve quality metrics that tie to reimbursement like reduced readmissions or increased screening rates to boost revenue.

· Preventative care using AI to identify at-risk patients, monitor chronic conditions, and encourage healthy behaviors could lower long-term costs from complications.

· Resource utilization optimizing staff schedules, operating room throughput, or imaging utilization based on AI demand forecasting to increase productivity.

· Reimbursement models working with payers to establish new codes recognizing value from AI-driven preventative care or remote patient monitoring that currently faces reimbursement barriers.

Clearly outlining tangible benefits, cost savings sources, and ROI timelines helps healthcare leaders justify AI investments that require significant upfront capital but promise to transform care delivery for years to come.

Ensuring Responsible, Ethical Development and Deployment

For AI to gain widespread trust in healthcare, solutions must be developed and applied responsibly with oversight mechanisms to ensure ethical, secure practices. Some considerations include:

· Data privacy — Strict policies around access, use, storage and sharing of sensitive patient information used to train AI models.

· Algorithmic bias — Auditing models for potential unfair treatment or inaccurate recommendations for disadvantaged groups due to biases in training data.

· Model transparency — For high-risk clinical uses, understanding “black box” models’ decision-making to ensure appropriate, non-harmful guidance.

· Regulatory compliance — Satisfying applicable laws around medical device clearance, clinical decision support tools, and clinical research with AI applications.

· Security safeguards — Robust protections against data breaches, hacking of connected devices, and other cybersecurity threats from bad actors.

· Clinical validation — Rigorous testing and evaluation of AI applications in real-world settings before high-consequence uses to prove safety, accuracy, and benefits.

Adopting responsible practices like these helps ensure AI develops trust among patients, providers, and policymakers that it will enhance — not jeopardize — healthcare quality and access.

Conclusion

When developed and applied strategically with careful oversight, AI has tremendous potential to digitally transform healthcare delivery for the better. By streamlining administrative workflows, enhancing medical imaging, powering precision medicine, and optimally fitting into clinical routines, AI can help address workforce shortages, expand access to expertise, and improve outcomes — particularly for underserved groups.

However, successfully realizing this potential requires addressing challenges around integration, usability, explainability, bias, privacy, security, and a clear return on investment demonstration. Ongoing collaboration between technology developers, providers, patients and regulators can help surmount these hurdles to build an AI-assisted future that places human well-being and judgment at the center of care. With the right safeguards and focus on augmenting — not replacing — clinicians, AI shows great promise to benefit patients and providers for years to come.

This article was written by Jon Warner, Executive Chair of Citizen Health Strategies (CHS)