Dr. Mary Krebs
Leveling the playing field: AI-assisted prior authorization can drive more equitable care
May 31, 2024
By Dr. Mary Krebs
Although healthcare access should be a fundamental right for all, the reality is stark: access to quality care and healthy living conditions varies dramatically across communities. Achieving health equity ensures everyone has an equal opportunity to live a long and healthy life, regardless of their background or zip code. Unfortunately, the path to health equity isn't always linear. It is essential to understand the many challenges of forging a path toward a truly equitable healthcare system.
Closing the health gap with SDOH data
As a physician who has seen the disparities in healthcare access and outcomes, particularly in underserved communities, I care about a patient's health far beyond what happens within the clinic walls. Social determinants of health (SDOH), like access to transportation, safe housing, quality education, and healthy food, play a vital role in a patient's health outcomes. This is why we see a rise in declining health in low-income neighborhoods and among underserved communities.
For example, a recent study found that a "patient's neighborhood and the hospital where they received treatment were both associated with risk of death after commonly performed inpatient surgical procedures." Living in areas with high poverty rates, low educational attainment, and poor housing quality heightened the risk of postoperative mortality for patients, irrespective of whether the procedure occurred at a top-tier hospital. Effectively capturing and utilizing this data is crucial for attaining health equity.
Unfortunately, there's a substantial obstacle to collecting this kind of data. Most of the time, we simply don't have it–it's like trying to navigate patient care with a blind spot. There are instances when a patient's zip code hasn't been captured for analysis. This makes it challenging to identify and address a patient's specific needs, especially if they're facing challenges outside of the medical domain. For example, if a patient doesn't have a safe place to recover after surgery, that may significantly affect their healing process.
The good news is things are changing and we are seeing areas of opportunity where patient data is being captured in more detail. The key to improving the process for everyone involved– patients, physicians, and health plans– is to share access to this information. In my previous practice, we screened for food insecurity. Once we identified patients struggling to access healthy food, we used their zip codes to connect them with local food banks. This made a world of difference for a diabetic patient who had previously relied on unhealthy food options that led to high blood sugar levels. Once the patient had access to nutritious food, their health improved dramatically.
Of course, it's not perfect. Even if we have the data, there's still a resource gap. For instance, physicians can't prescribe transportation assistance. We need a system that collects this data and incentivizes addressing these social factors. Thankfully, the healthcare industry is focusing on innovation and equity. By using AI technology to capture and analyze SDOH data on a granular level, physicians and health plans can tailor interventions to a patient's needs. However, challenges persist, including prior authorization delays, the lack of reimbursement for non-medical services, and the strain on physicians' time.
Harnessing AI for health equity
Prior authorization, a process requiring approval before certain medical services are delivered, can be a double-edged sword. While it helps control costs, it can also create administrative burdens for physicians and delays for patients. Despite challenges, innovation in healthcare offers hope. I fully support solutions used to solidify health equity, including using intelligent prior authorization as a catalyst for building a more equitable healthcare system. When used extensively, AI and machine learning technologies have the potential to analyze SDOH data and create personalized care pathways. These pathways consider clinical and social factors, ensuring patients receive tailored interventions regardless of background.
Intelligent prior authorization has the potential to be a powerful tool for promoting health equity by empowering physicians, streamlining processes, and personalizing care. Here's a closer look at three ways the healthcare system can achieve this.
Ensuring responsible AI implementation
While AI and machine learning technology offer potential benefits for promoting health equity, it's crucial to underscore the necessity of addressing algorithmic bias, maintaining clinical oversight, and securing patient data. Health plans must use diverse datasets and never employ AI technology to deny care.
By harnessing SDOH data and empowering physicians with intelligent tools, this technology can personalize care plans that address both medical conditions and social determinants of health. The future holds immense promise for improved patient outcomes–and a healthcare system where interventions are truly tailored to ALL patients' needs.
About the author: Dr. Mary Krebs serves as Medical Director of Primary Care at Cohere Health. In addition, she teaches residents and medical students at a family medicine residency program in Dayton, Ohio. She earned her medical degree from the Ohio State University College of Medicine in Columbus and completed a family medicine residency at Miami Valley Hospital in Dayton, Ohio. Previously, Dr. Krebs was in solo practice at a rural federally-qualified health center and co-ran Family Practice Associates, an independent rural practice.
Although healthcare access should be a fundamental right for all, the reality is stark: access to quality care and healthy living conditions varies dramatically across communities. Achieving health equity ensures everyone has an equal opportunity to live a long and healthy life, regardless of their background or zip code. Unfortunately, the path to health equity isn't always linear. It is essential to understand the many challenges of forging a path toward a truly equitable healthcare system.
Closing the health gap with SDOH data
As a physician who has seen the disparities in healthcare access and outcomes, particularly in underserved communities, I care about a patient's health far beyond what happens within the clinic walls. Social determinants of health (SDOH), like access to transportation, safe housing, quality education, and healthy food, play a vital role in a patient's health outcomes. This is why we see a rise in declining health in low-income neighborhoods and among underserved communities.
For example, a recent study found that a "patient's neighborhood and the hospital where they received treatment were both associated with risk of death after commonly performed inpatient surgical procedures." Living in areas with high poverty rates, low educational attainment, and poor housing quality heightened the risk of postoperative mortality for patients, irrespective of whether the procedure occurred at a top-tier hospital. Effectively capturing and utilizing this data is crucial for attaining health equity.
Unfortunately, there's a substantial obstacle to collecting this kind of data. Most of the time, we simply don't have it–it's like trying to navigate patient care with a blind spot. There are instances when a patient's zip code hasn't been captured for analysis. This makes it challenging to identify and address a patient's specific needs, especially if they're facing challenges outside of the medical domain. For example, if a patient doesn't have a safe place to recover after surgery, that may significantly affect their healing process.
The good news is things are changing and we are seeing areas of opportunity where patient data is being captured in more detail. The key to improving the process for everyone involved– patients, physicians, and health plans– is to share access to this information. In my previous practice, we screened for food insecurity. Once we identified patients struggling to access healthy food, we used their zip codes to connect them with local food banks. This made a world of difference for a diabetic patient who had previously relied on unhealthy food options that led to high blood sugar levels. Once the patient had access to nutritious food, their health improved dramatically.
Of course, it's not perfect. Even if we have the data, there's still a resource gap. For instance, physicians can't prescribe transportation assistance. We need a system that collects this data and incentivizes addressing these social factors. Thankfully, the healthcare industry is focusing on innovation and equity. By using AI technology to capture and analyze SDOH data on a granular level, physicians and health plans can tailor interventions to a patient's needs. However, challenges persist, including prior authorization delays, the lack of reimbursement for non-medical services, and the strain on physicians' time.
Harnessing AI for health equity
Prior authorization, a process requiring approval before certain medical services are delivered, can be a double-edged sword. While it helps control costs, it can also create administrative burdens for physicians and delays for patients. Despite challenges, innovation in healthcare offers hope. I fully support solutions used to solidify health equity, including using intelligent prior authorization as a catalyst for building a more equitable healthcare system. When used extensively, AI and machine learning technologies have the potential to analyze SDOH data and create personalized care pathways. These pathways consider clinical and social factors, ensuring patients receive tailored interventions regardless of background.
Intelligent prior authorization has the potential to be a powerful tool for promoting health equity by empowering physicians, streamlining processes, and personalizing care. Here's a closer look at three ways the healthcare system can achieve this.
- 1. Episodic authorizations for streamlined care:
Prior authorization often requires separate approvals for each step in a treatment plan, creating a bureaucratic maze, especially for complex conditions. AI and machine learning can analyze the entire treatment plan and recommend an "episodic authorization" that covers all the necessary services in a single step. This streamlined process is particularly helpful for underserved communities who may already face challenges accessing healthcare. Additionally, reduced administrative burden allows physicians to focus more on patient care, and patients experience fewer delays in receiving essential treatments.
2. Empowering physicians with real-time clinical guidance:
When a prior authorization is submitted, AI and machine learning technology can instantly analyze the patient's medical history, condition, and evidence-based guidelines. Instead of a simple approval or denial, the doctor receives a real-time clinical "nudge" that suggests alternative treatment options, highlights additional information needed to complete the request, or even recommends collaboration with specialists for complex cases. This two-way communication assists physicians to make informed decisions, ensures patients receive optimal care tailored to their needs, and reduces denials due to incomplete or missing information.
3. Personalized care pathways with AI-powered clinical insights:
AI and machine learning can go beyond analyzing medical records; they can consider SDOH factors that significantly impact health outcomes. For example, the technology can review a prior authorization request for a patient predisposed to heart disease and analyze their medical history, lab results, and genes to assess their risk. AI has the ability to go beyond the basics of analyzing data from wearable devices to understand a patient's lifestyle habits and recommend personalized improvements, while also considering social determinants of health, like access to healthy food, to suggest relevant resources.
Ensuring responsible AI implementation
While AI and machine learning technology offer potential benefits for promoting health equity, it's crucial to underscore the necessity of addressing algorithmic bias, maintaining clinical oversight, and securing patient data. Health plans must use diverse datasets and never employ AI technology to deny care.
By harnessing SDOH data and empowering physicians with intelligent tools, this technology can personalize care plans that address both medical conditions and social determinants of health. The future holds immense promise for improved patient outcomes–and a healthcare system where interventions are truly tailored to ALL patients' needs.
About the author: Dr. Mary Krebs serves as Medical Director of Primary Care at Cohere Health. In addition, she teaches residents and medical students at a family medicine residency program in Dayton, Ohio. She earned her medical degree from the Ohio State University College of Medicine in Columbus and completed a family medicine residency at Miami Valley Hospital in Dayton, Ohio. Previously, Dr. Krebs was in solo practice at a rural federally-qualified health center and co-ran Family Practice Associates, an independent rural practice.