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Can AI help doctors fight burnout?

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Even before COVID-19, 40% of doctors said they felt burned out. But the pandemic was a tipping point. In the first year of the pandemic alone, more than 3,600 U.S. health care workers in jerry-riged PPE died in overcrowded, understaffed ICUs. After witnessing the lonely death of about 1 million patientsWhile holding the phone while sharing their last minutes with family members via FaceTime, more doctors are deciding to retire early, exacerbating an impending shortage. A report last year by the Association of American Medical Colleges predicts a shortage of up to 124,000 physicians by 2034. That includes a gap of as many as 48,000 primary care physicians, who report higher levels of burnout than other specialties. And it’s not just doctors: In a January 2022 survey by Prosper Insights & Analytics, just 50% of all health professionals said they were “happy” at work.

You don’t buy happiness overnight. Filling the staff shortages will take time. But in the meantime, proponents say, artificial intelligence (AI) could be used to lighten the burden of maximum MDs. “We need to make every doctor a super doctor,” said Farzad Soleimani, an assistant professor of emergency medicine at Baylor College of Medicine and a partner at 1984 Ventures, a San Francisco-based VC firm. “Ultimately, clinicians learn to recognize patterns. That is the power of AI.”

Of course there are doubters. An April 2019 Medscape survey of 1,500 physicians in Europe, Latin America, and the US found that a majority were anxious or uncomfortable with AI, with US physicians expressing the most skepticism (49%). Relying on patient care algorithms also poses ethical, clinical and legal issues. AI can pose significant threats privacy issues, ethical concerns and medical errors. Developers can unknowingly introduce biases into AI algorithms or train them using flawed or incomplete data sets. Data used to train AI systems can be vulnerable to hacking. By delegating aspects of decision-making to machines, doctors could lose their traditional autonomy and authority – and notions of accountability will be tested if AI-led recommendations lead to patient harm.

Nevertheless, huhHealthcare AI companies, including nearly 500 early-stage startups, raised a record $12 billion last year, according to CB Insights. Here are just a few of the ways tech companies are using deep learning algorithms and natural language processing to automate routine tasks in hospitals, reduce hours spent by healthcare providers on paperwork, and reduce errors caused by fatigue

Speed ​​up pre-visit evaluations

Managing patients and preventing caregiver burnout begins before care recipients even show up in the office or hospital. Based on San Francisco health note streamlines patient admission with a text-based AI chatbot that collects patient information prior to the visit and automatically writes notes for their physician, reducing admission and documentation time by up to 90%, the company said. Decoded health– a spin-off of SRI International, the non-profit research organization that developed the technology behind the computer mouse, ultrasound and Siri – offers what it calls a “virtual medical resident” that pre-screens patients using natural language processing, and a summary turns their medical complaints into useful care advice. Keona Health focuses on helping nurses and non-medical personnel perform triage over the phone, guide them through symptom monitoring, provide care recommendations, and automate appointment scheduling.

Help with triage

When the emergency room is hit, AI triage tools are designed to help flag patients who need critical care and could otherwise be missed, highlight the most serious cases, and prioritize care. The first major clinical application of AI triage tools was in radiology; companies including: RapidAInamely aiand arteries they all have FDA approval for algorithms that detect signs of stroke, brain haemorrhage and pulmonary embolism on CT scans. imageThe FDA-approved OsteoDetect analyzes X-rays of the wrist to detect distal radius fractures, one of the most common injuries to the joint. MednitionKATE, the real-time triage counseling tool, analyzes EHR data and patient vitals collected at intake to help emergency nurses recognize warning signs of sepsis, which are responsible for more than 50% of hospital deaths. It is used throughout the Adventist Health system and others to prevent ER withdrawals from previous treatment. Using ERs from Johns Hopkins University stocasticTriageGO, which analyzes vital signs and other admission data, along with patient demographics and medical history to make rapid care recommendations, reducing door to decision time by up to 30 minutes.

Overwrite Doctor’s Notes

A recent study found that physicians spend an average of about 16 minutes on electronic health records for each patient visit. DeepScribe is a speech-based digital assistant that allows a doctor to have a normal conversation with his patient, transcribe it, extract important information from it and automatically place it in the appropriate sections of the medical records. In January 2021, the San Francisco-based startup raised $30 million. Competitors include: nuancesukiand Cortic

There is also Rad AI’s Omni Softwarea virtual assistant designed specifically for radiologists that helps write a formal “clinical impression” based on dictated notes, automatically inserts guidelines recommendations, and detects potential errors.

Manage the billing process

“When people talk about health care workforce shortages, they often think of nurses, doctors, and primary care workers, but the problem is organization-wide,” said Ben Beadle-Ryby, co-founder of South San Francisco-based AI-based Akasa. health care services. According to recent surveys by the Healthcare Financial Management Association, more than 57% of health systems and hospitals have more than 100 open back-office roles in billing, record-keeping and scheduling to fill. A recent Change Healthcare survey found that 78% of healthcare leaders are already using AI in their revenue cycle management and 98% expect to do so by 2023.

akasa provides services to more than 475 hospitals and health systems and more than 8,000 outpatient clinics in all 50 states, using a constant-learning AI system to help them automate insurance claim status checking, pre-authorization, eligibility and deniability management . Privia Health provides scheduling and billing tools for some 3,300 independent physicians, using robotic processing automation, where an intelligent system learns a scripting process for handling repetitive billing tasks as a human would.

Help with testing

Lab tests determine about two-thirds of doctors’ decisions. Before COVID-19, medical lab professionals performed about 13 billion lab tests per year. In a February 2020 survey by the American Society for Clinical Pathology, more than 85% of medical lab workers reported burnout; 36.5% complained about insufficient staff. That was before the added burden of conducting more than 900 million COVID-19 tests since the start of the pandemic. Many hospital labs have 10% to 35% staff vacancies.

By automating repetitive work, fewer people can do more and perhaps improve results. In a 13-month pilot, the University of Texas Medical Branch hospital in Galveston Biocognitive‘s “laboratory intelligence platform” to help process more than 325,000 COVID-19 tests and create personalized interpretations based on PCR and antibody tests, patient data, and medical history. The result: a nearly doubling in efficiency, less escalation to intensive care and lower death rates. “COVID-19 was a time of tremendous change, both clinically and operationally,” said Peter McCaffrey, MD, an assistant professor of pathology at the hospital and director of pathology informatics and laboratory information systems. “With Biocogniv’s platform, we were able to scale interpretation and guidance for COVID and coordinate everyone during this time of unprecedented uncertainty.” In the pipeline from the company: laboratory-based prediction tools for sepsis, respiratory failure and acute heart failure.

Whether AI proves itself in each of these areas or not, there is no turning back. “By minimizing or relieving repetitive diagnostic tasks, [AI can help] physicians spend more time on advanced clinical reasoning and assessment, and inherently human work, such as working with multidisciplinary care teams to support patient care,” said Mark Schuster, MD, pediatrician and founder and dean and CEO of the Kaiser Permanente Bernard J. Tyson School of Medicine in Pasadena, California. In addition to addressing physician specialty shortages in areas such as radiology, where AI has proven highly accurate, Schuster expects clinical care algorithms to become more powerful, with “a gradual increase in precision and personalization of diagnosis and treatment.” Still, he recognizes the potential danger that AI could amplify the biases that already exist in healthcare. “We recognize,” he says, “that there remains a significant risk of introducing unmeasured biases through machine learning into AI.”

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