Creating Better Medical Practices With AI

The healthcare sector is undergoing a transformation driven by artificial intelligence (AI). This transformation brings with it new standards of patient care, diagnosis, and treatment efficacy. Worldwide, healthcare systems face challenges ranging from improving patient outcomes to providing efficient patient care. These challenges require innovative solutions and engender technologies that will help achieve new medical practices.

AI has incredible analytical capabilities and offers new opportunities to the healthcare industry, such as enhanced diagnostic precision by leveraging vast datasets and identifying patterns and anomalies. AI does not diminish the doctors’ roles but enhances their ability to make decisions with swift and accurate diagnoses. With AI-driven insights, doctors can validate findings with their expertise and understanding of patient care.

But AI's impact goes beyond diagnostics into predictive analysis in patient care to personalized treatment plans. It could optimize healthcare delivery. These advancements could lead to a reduction in wait times, targeted therapies, and better health outcomes.

Acknowledging AI's capacity requires a balanced conversation, considering the ethical, privacy, and data security concerns associated with integrating such technology into healthcare.

The goal is to create a working relationship between AI and medical professionals, where the technology heightens human expertise instead of replacing it. Collaboration paves the way for a future where healthcare is more accessible, efficient, and effective.

Transitioning to the Digital Age

Historically, medical records were kept as handwritten or transcribed hardcopy notes for every patient visit, treatment, and progress. When computers became commonplace, medical data became stored electronically; but even then, it took many years for doctors to adjust to this new system. Early on, health professionals used non-networked, closed computer systems to store and retrieve patient information locally as needed.

Internet connectivity allowed software companies to serve the medical community with data capture, report generation, relational database management, and, more importantly, medical billing and accounting. Insurance companies changed the operational aspects of medical practice, requiring medical practitioners to code records in very specific ways. Computerized data systems could help file insurance claims electronically via modems and faxes. There was hope that this would allow doctors to spend more quality time with their patients. While it indeed saved time, it encountered initial challenges.

In 1996, the US Congress introduced the Health Insurance Portability and Accountability Act (HIPAA) to centralize and protect patient health information. However, the act inadvertently imposed significant challenges on small medical practices. Transitioning years of hardcopy patient records into a HIPAA-compliant, searchable database proved costly and labor-intensive without the option to use simple scanning methods. Even filing insurance claims required some expertise and posed additional challenges.

HIPAA tried to integrate all health information with the goal of preserving privacy and security, as well as reporting breaches. Using an electronic protected health information (ePHI) approach, all protected health information produced, saved, transferred, or received electronically needed to conform to HIPAA’s rules. Only authorized users could see the data.

Centralizing these data streamlined how health professionals, agencies, insurers, and statistical data-gathering arms of the government (such as the US Centers for Disease Control and Prevention (CDC) and its Environmental Public Health Tracking (EPHT) Network) shared information. This aimed to improve efficiency and the quality of care through digital innovation.

Ambient Clinical Intelligence

One such innovation is ambient clinical intelligence (ACI), which uses speech recognition and voice recording to capture interactions between patients and medical staff. Using auto-dictation for record-keeping reduces the number of practitioners who need to attend to a patient. For example, instead of requiring an assistant to take notes during an examination, a computer with ACI listens, records, and transcribes all interactions between the physician and the patient. The assistant can then perform other tasks as needed.

When AI is combined with this data exchange, it can help eliminate or reduce transcription errors, and the physician can continue with the examination. While it is possible today to use standard hardware to implement some of these functions, pervasive AI technology is expected to play a more dominant role, especially with context and medical pattern recognition for diagnosis and prognosis assistance.

For example, medical monitoring services could combine AI with sensors and video feeds to detect a patient's fall. Phones and medical alert pendants can do that now, but patients may remove their pendants or may not be near their phones. However, an AI-enabled video system can detect if the pendant is removed while still detecting a fall.

As AI has evolved, staff can now create documents automatically and format data to comply with electronic health records standards. This simplifies clinical review and helps to ensure timely filing of data and claims. It can also reduce—possibly eliminate—burnout among medical professionals who have been overworked and not given as much time with patients as they would like.

How ACI Works

Initially, ACI will function like a digital butler designed for transcription, documentation, coding, formatting, storage, and transmission. But AI medical technology will continue to evolve, with increasingly larger data sets as health sensors are placed or implanted in patients. With continuous monitoring of patients' stats, AI that has access can be trained on anonymized, ultra-large, HIPAA-compliant datasets. AI will be not only making diagnoses but also discoveries that can further advance medical knowledge and treatment modalities.

By accessing this (anonymized) information, the AI could discover new potentially hazardous drug interactions. For example, an AI may discover that a statistically significant number of patients taking medication A for a specific affliction and medication B for another affliction develop condition C.

Implanted and worn sensors are not the only tools AI health and wellness monitoring technology can use. Computerized video processing can determine heart rate and blood pressure by monitoring blood vessels humans may not notice or even be able to see.

The combined knowledge and experience of humans and AI can be used to diagnose patients more effectively than ever before. AI trained with large data sets and experience has already demonstrated its ability to read biopsy data and even catch cancers that experienced doctors have missed.^[1]

What does this mean for medical facilities? In the short term, it will help unburden staff from mundane paperwork and filing. It will allow doctors to spend more time with their patients, possibly leading to more reliable diagnoses. It may also help reduce the number of practitioners and staff needed to operate a medical facility under normal conditions.

Future Considerations

As medical facilities continue to navigate regulations like HIPAA, insurance companies' requirements, and evolving patient-doctor relationships, the healthcare sector is looking for new solutions to enhance efficiency and reduce error. Medical malpractice is real, and the industry is prone to human error.

Integrating AI and robotics into healthcare offers exciting opportunities to complement human expertise with the precision and recall of AI technologies. Of course, questions arise with new technologies, including cost, government support, data oversight, security, and the logistics of system updates. Cloud-based and corporate entities are emerging to assist medical practitioners with their filing and coding, signifying a trend toward digital transformation in the healthcare industry.

With consistent advancements in dexterity, vision, and data processing, robotics will eventually emerge as an even more valuable tool in medical procedures (Figure 1). When we look to the future, the key will be a balanced approach and a collaboration between technology and humans to advance healthcare and maintain trust.

Figure 1: As medical robotic technology improves, robotic surgeons may increasingly perform tasks that humans previously performed. They have better vision, dexterity, and a deeper knowledge base. Image credit: Futek This AI revolution in healthcare means that medical training will have to change. Doctors may need to focus on more personalized medicine that leverages large datasets to predict, prevent, and cure diseases effectively and efficiently. Medical curricula may include data science, machine learning, and ethical considerations in AI technology. Transitioning into this AI-driven healthcare era means doctors must understand privacy concerns in digital healthcare.

Centralizing personal and medical data raises security concerns, as data is always susceptible to breach. While data may be encrypted, it is not guaranteed to be safe; even the most prominent companies with the best IT departments have been breached. With continuous advancements in cybersecurity and data protection protocols, the healthcare industry is becoming increasingly adept at safeguarding this sensitive information, ensuring that the benefits of data centralization can be realized while minimizing risks.

Conclusion

As the medical field evolves to integrate AI, it must balance technological advancements and data security. While there will always be risks associated with digitizing aspects of our lives, the potential of AI to revolutionize healthcare by improving efficiency and patient outcomes is inspiring. As healthcare industries embrace AI while staying abreast of cybersecurity measures, a brighter future that changes how we treat patients and save lives awaits.

Sources

[1]  “More Breast Cancers Detected in First Evaluation of Breast Screening AI,” University of Aberdeen, March 21, 2024, https://www.abdn.ac.uk/news/22964/.