As healthcare worker burnout continues to be a major concern, the use of artificial intelligence, EHR, and other automation tools can have a positive impact on hospitals and health systems. economic.
When it comes artificial intelligence, some legal problems arise. That’s why we interviewed Carly Koza, an expert on the subject and partner of Buchanan Ingersoll & Rooney. Buchanan Ingersoll & Rooney is a national law firm with 450 attorneys and government relations specialists in 15 offices representing companies including 50 Fortune 100 companies.
Koza discusses what healthcare delivery organizations should be prepared for as AI deployments increase, how AI can help address growing demand for healthcare workers, how AI can can help healthcare delivery organizations ensure the quality of patient care and legal issues arising from these issues.
Q. What should healthcare providers prepare for as AI deployments increase in the near future? Legal problems arise?
ONE. Traditionally, providers alone assessed patients and directly provided observation-based medical information, but AI is changing the healthcare industry. AI can analyze data stored by healthcare delivery organizations to find possible connections between patients, diseases, and treatment.
Data analytics can be divided into four distinct categories – diagnostic, descriptive, prescribing, and predictive – all of which evaluate both current and historical data to predict certain outcomes when they relate to an individual.
Healthcare delivery organizations should be prepared to use both clinical decision support and image recognition software to provide personalized diagnoses and treatment recommendations to patients. .
For example, when properly applied, clinical decision support software can make treatment recommendations to oncologists, based on what the program has learned from large amounts of patient data. , as well as information it derives from references, clinical guidelines, and medical journals.
In addition, image recognition software and predictive analytics can help providers recognize skin cancers in images, identify arrhythmias in an electrocardiogram, and determine if a nodule on a CT scan is present. malignant or not. Essentially, predictive analytics can assist providers in identifying which patients are at risk of being re-hospitalized and whose health conditions require intervention.
However, as AI is constantly improving, physicians should consider the informed consent principle because of possible liability if the physician fails to inform the patient of the risks and benefits of the drug. suggested treatment and no treatment.
To avoid any legal problems that may arise from the best interpretation of data, when making a diagnosis or making treatment recommendations to a patient, physicians should disclose the ever-evolving nature of the data. of the technology used, explain how the AI system works, and share the extent to which the technology is trusted because when widely used, AI can even be seen as another member of the medical team.
Finally, AI clinical decision support programs learn from the health information of some patients through data collection to make recommendations for others.
Therefore, treating physicians should address potential security and privacy issues that may arise as patients have the right to know how their protected health information is being disseminated. for research, diagnostic and therapeutic purposes.
Q. How can AI help address the growing need for healthcare workers? Are there any legal issues that could affect healthcare workers?
ONE. AI can assist with administrative workflows. Since AI stores aggregated data, AI can help correct gaps in medical records and generate automated responses and fills. In addition, aggregating thousands of patient data creates streamlined processes for health systems to improve quality of care while maximizing profits.
Recording chart notes and patient summaries, along with writing test orders and prescriptions, can keep providers out of patient care. To alleviate this problem, healthcare practitioners can use voice and text recognition for tasks such as recording patient communications and recording clinical notes.
The use of AI can also be beneficial when applied to payment and claims handling as well as prior authorization. Robotic Process Automation, a rule-based software robot that mimics human actions, can be used to enter, process, and modify orders. Natural Language Processing, a form of AI that can understand and interpret human spoken or written language, can convert physician notes into standardized CPT and ICD-10 codes related to chemistry. single.
Prior authorization includes confirmation of each patient’s individual health plan and any medical and medication benefits, along with any additional patient information that may be needed to approve treatment. , including history, referral information, or medical necessity.
As health systems continue to expand, the administrative burden of manually entering or finding said information can be reduced by a greater reliance on AI that can organize information from records. e-health, email, health policies and protocols.
By using computer algorithms and natural language processing to assimilate spoken or written data, clinicians can more effectively deliver informed treatment plans to patients. .
While AI can be used as a tool to combat the growing demand for healthcare workers, it’s important to consider bias. For example, health care providers must closely monitor prior authorization decisions for disadvantaged populations and Medicaid recipients.
The inherent bias of programmers developing algorithms used in AI systems, whether intentional or unintentional, often leads to further bias in algorithms and data from the main source. WHO.
For example, a facial recognition algorithm can be programmed to more easily recognize a Caucasian individual than individuals of other races, since this data is used more often in training. Biased input that does not take into account the overall number of patients can lead to skewed results. Therefore, the best possible way is to assess potential bias by completing risk assessments and ethics training.
Q. In what ways can AI help healthcare delivery organizations ensure quality patient care and reduce potential liability?
ONE. AI can help healthcare delivery organizations ensure quality patient care at both the individual and population levels. On a personal level, AI-insights data can provide practitioners with progress updates, detailed history and other patient-related information to better ensure proper treatment. and help predict future outcomes.
Providers can also use AI to study broad community-relevant factors related to population health. If healthcare providers rely on AI to target high-risk patients grouped by clinical condition or comorbidities, studies show they can expect better outcomes. interventions are becoming less severe and reduce hospital admissions.
Additionally, when the analytical power of AI is combined with other technologies such as robots and their physical abilities, even stem cell processes used in regenerative medicine could improve. and help restore or establish normal function in cells, tissues or organs. By spending less time on lengthy processes and manual tasks, providers can spend more time on patient-centered care.
In addition to vendors’ implementation of AI into their respective practices, manufacturers and developers are also involved in AI development. Therefore, it is important to consider their role when aiming to reduce potential supplier liability.
Manufacturers and developers must ensure that products containing AI are safe when used in reasonably predictable ways so that healthcare organizations avoid implementing the system. faulty AI systems into their reality.
The healthcare provider organization should then assess the impact of the automated systems they choose to use and consider the extent to which they are reliant on these systems to make informed choices when administering care. treat patients.
AI is a useful tool, but ultimately the diagnostic or treating physician is responsible for medical decisions and therefore also if the care provided was negligent or reckless. .
By using artificial intelligence to pair high-quality data sets together with predictive algorithms, in addition to their medical knowledge, doctors can form complex connections between multiple patient characteristics and subsequently provide a more accurate diagnosis, while reducing potential liability.
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Healthcare IT News is a publication of HIMSS Media.
