What Happens When Artificial Intelligence or ChatBots in Healthcare Collide with Prior Authorization Rationing?
One doesn't need to read the work of rehabilitated Health Insurance Corporate Executive Wendell Potter (https://www.wendellpotterconsulting.com/) to understand that private health insurance companies make money for their shareholders, bondholders, executives and bureaucrats by fiscally and physically rationing access to physicians, hospitals, diagnostics and treatments. This physical and fiscal rationing of preventive, medical, surgical and palliative care by insurance companies results in altering clinical outcomes for patients with identical illnesses among different insurance companies and plans.
Insurance companies physically ration access for-profit by limiting the number physicians and hospitals in a community which can care for a patient contracted with that insurance plan. Elevated co-payments, deductibles and premiums ration access for-profit using fiscal restraints. Reimbursement rationing to physicians via underpayments or refusing payment to physicians relative to contracts is a profit method unique to the health insurance industry. Withholding or delaying pay as insurance companies do with doctors occurs on a large scale daily has not been seen in America since the days of slavery or sharecropping where risk and work was not reimbursed by their corporate employers or landowners.
Another common and incredibly effective rationing tool to enhance insurance company profits is 'prior authorization'. Prior authorization forces millions of patients and physicians daily to manually complete multiple pages of paper forms (such as in the photo above) for re-submission to a non-physician insurance industry bureaucrat who after days or weeks or months of delay decides if the physicians recommended diagnostics or treatments for his or her patient will be reimbursed or allowed by the insurance company. https://www.cnn.com/2018/02/11/health/aetna-california-investigation/index.html There is not a patient or physician in America with private health insurance who hasn't experienced the demeaning and potentially dangerous task of manual prior authorization healthcare rationing. Basically, whats good for the patient based on the Physicians Non-Artificial Intelligence (NAI) may be deleterious to the EBITDA of the Insurance or Pharmaceutical Benefits company, and thus the workflow horror and clinically dangerous prior authorization rationing was spawned.
Physicians currently support another ancillary healthcare industry, the EHR industry, by paying EHR corporations for the privilege of inputting their patient data, labs and photos so that the EHR company can tabulate and sell that private patient data to ancillary healthcare corporations. Currently, the patient-generated physician-inputted data is tabulated by the EHR companies solely for industry profit and not to improve patients clinical outcomes or costs. In the future EHR companies may assist physicians NAI and their patients by delivering artificial intelligence using the data analysis of the aggregated patient population. This application of population health AI data will occur only if the EHR companies will both standardize to collect community wide statistically significant data and at the same time profit from the function by charging the physicians for the tabulated population data analysis.
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Without concomitant software to overcome prior authorization rationing of prescriptions by insurance companies and Pharmacy Benefit Managers or built-in EHR software to override diagnostic and treatment rationing by insurance bureaucrats, the benefits of AI clinically for the patient or physician will never be applied at the bedside.
Think about this, sometime in the future, an EHR company or Amazon Comprehend Medical AWS using nation wide interoperable EHR machine learned-AI population value data analysis suggests to the physician a mode of prevention, diagnostic test, examination or treatment for a specific patient which differs from the physicians NAI assessment and plan. Next, the patients insurance company or PBM using prior authorization rationing techniques recognizes the AI suggested preventive, medical, surgical or palliative diagnostics or treatments as deleterious to the profits of their Corporation, shareholders, bondholders, executives, administrative bureaucrats and patron politicians. In turn, the insurance company or PBM refuses the EHR generated AI, and makes these EHR generated diagnostic or treatment suggestions unobtainable for the patient or their doctor.
This function of automated overriding of prior authorization rationing of Artificial Intelligence (or NAI) suggestions could be easily delivered to physicians simply by cross-linking insurance company drug formularies with individual patients insurance plans using several prescription tracking companies used daily in most pharmacies. Currently there are no EHR's in America which are populated with per individual insurance plan drug prices, medication co-pays, deductibles and drug formularies even though this software is used by pharmacists in all pharmacies and drug stores. In addition, to assisting in overriding, diagnostic and treatment prior authorization rationing, AI diagnosis or treatment suggestions can be directly interoperated with the computer algorithm of the insurance company bureaucrat's who oversees and controls patients diagnosis and treatments. I'm betting, the low earnings and low profitability potential of prior authorization API overriding software for the EHR industry combined with data (price and formulary) blocking by Pharmaceutical Industry Benefit Managers (PBM's) and the insurance companies along with the legal risk of a 'transparent' machine making diagnostic and treatment decisions will prevent implementation or this most desired clinical function.
Without coupling prior authorization rationing relief software, EHR company generated AI will simply end up 'pushing on a string' placing the physician and patient at risk while simultaneously charging the physician for the clinically useless AI, which is the current modus operandi for the EHR and Health Insurance Industries.
Explorer, MD, PhD | Physician, Scientist, Clinical Informatics, AI, ML | CMO, VP, Board Member | Diversity & Health
1 周2017...
Physician | Futurist | Angel Investor | Custom Software Development | Tech Resource Provider | Digital Health Consultant | YouTuber | AI Integration Consultant | In the pursuit of constant improvement
7 个月Very true. Your observations highlight a significant issue in the healthcare system. The profit-driven rationing by private health insurance companies indeed impacts patient care by limiting ACCESS and increasing COSTS. It's concerning how the EHR industry similarly prioritizes PROFIT over patient outcomes, with physicians bearing the cost of inputting data. For AI-driven improvements to genuinely benefit PATIENTS, we need STANDARDIZED data collection and a focus on CLINICALLY useful applications. How can we drive these industries to adopt more PATIENT-CENTERED practices while ensuring they remain economically viable?
Nurse Practitioner @ Southwest Florida Medicine PLLC | Psychiatric Mental Health
1 年How do we opt out VIA exercising our HIPAA rights? ??????
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1 年Risk stratification can play a role in improving the prior authorization process mentioned in the post. By utilizing AI and risk stratification techniques, insurance companies can streamline the process and reduce delays. Instead of manual paper forms, AI algorithms can assess the patient's risk profile based on their medical history and relevant data, allowing for faster evaluation of treatment recommendations. This can lead to more efficient decisions, timely access to necessary diagnostics or treatments, and a smoother experience for both patients and physicians.
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1 年Excellent points! Well made Dr. Green! As ChatGPT would tell you, "Addressing these issues will require a collaborative effort between insurance companies, healthcare providers, and patients to develop more transparent, consistent, and patient-centered processes for prior authorization." AI can operate with in the context of prior authorization, and can make suggestions about how to fix it, but only NAI decision-makers can fix it.