Forget the Medical Chatbots. Let’s use AI to Fix Healthcare Administration.

December 21st, 2017

I am puzzled by the interest in AI medical diagnosis chatbots. Dozen of startups are tackling one of the most difficult technical problems — clinical diagnosis — with fairly immature technology and no real business model. It’s even more intriguing when you realize there are billion dollar opportunities in administrative process automation, which is a massive pain point for almost every medical practice in the country. It’s also much simpler than algorithmic medical diagnosis.


using artificial intelligence (AI) for healthcare administration


Context: We’re Spending Hundreds of Billions of Dollars on Automatable Clerical Work

Unless you’ve worked at a provider organization or insurer, you probably aren’t familiar with the colossal administrative engine that makes our healthcare system tick.


Let’s start with the basics. We spend about $3.4 trillion annually on healthcare in the United States. About one third, or $1 trillion, is spent at hospitals. About 25% of all hospital spending — over 1% of GDP — doesn’t go to care delivery. Instead, these dollars — about $250 billion, or $750 per American per year — fund hospital administration (per a 2014 Health Affairs study).


Some of those expenses are necessary. Hospitals are complex organizations that require many non-clinical staff. However, a large portion of that expense is clerical work that adds no value for the patient.


Keep in mind that the $250 billion figure only covers hospital-based administrative expenses.


About 25% to 30% of healthcare spending is on ambulatory and outpatient care, which includes most healthcare services that occur outside the hospital, such as primary care, specialty care, outpatient surgeries, and diagnostic services. Benchmarks suggest that at least 20% of all ambulatory spending goes toward administrative support staff. In fact, the average primary care provider in a private practice is supported by almost five non-clinical staff (per Medical Group Management Association benchmarks). Ambulatory and outpatient administrative expenses add another ~$170B in costs each year.


If only 25% of this $420B in annual expense is automatable or augmentable clerical work, we arrive at a total opportunity of over $100B in annual costs. Though some of those costs are outsourced offshore, the domestic clerical burden is enormous. Nearly 60,000 Americans work as medical transcriptionists, over 200,000 are medical records technicians, and another 630,000 act as medical assistants (per the Bureau of Labor Statistics). I didn’t even include the administration expenses incurred at insurance companies, pharmacies, home health, nursing homes, device suppliers, or in government — which makes up the other ~40% of healthcare spending.



Enter AI

While outpatient practices are rife with operational inefficiencies, I’m going to focus on three clerical (i.e., non-clinical) areas where AI — or its simpler cousin, Robotic Process Automation — can yield the most value.


1. Revenue Cycle & Credentialing

Revenue cycle management (RCM) is the core of getting paid in healthcare. It’s a very complex topic with many opportunities for automation. RCM tools offload some of the complexity to rules engines but still leave a lot of manual processing to humans. In addition, credentialing a clinician with an insurance company — a process in which the insurer validates that the provider is eligible to render care as an in-network provider — is a tedious and time-consuming exercise that adds to a practice’s administrative load.



  • Coding & Claim Generation: Practice staff have to create electronic claims after each visit based on the notes in the electronic medical record. They have to scrub each claim based on a series of rules to ensure it will get adjudicated correctly by the insurance company, and these rules can change at any time without advance notice.
  • Denials & Appeals: Resubmit rejected claims. Often involves calling insurance companies, reviewing coverage documentation, and consulting coding guidelines to select the appropriate codes for each insurance company.
  • Credentialing: Submitting detailed personal and educational histories for every billing provider to each insurance company every few years.



2. Insurance Administration

In addition to submitting claims for payment, practice staff spend a lot of time helping patients and clinicians navigate the insurance system.

  • Evaluating Insurance Coverage: A patient receives a referral from her PCP to a cardiologist. The practice staff call the insurance company to confirm the cardiologist is in-network and accepting new patients.
  • Managing Insurance Authorizations: A patient receives a referral from his PCP for an abdominal CT to rule out appendicitis. In order for the insurance company to cover it, a practice administrator needs to log into a portal and provide documentation. Alternatively, an insurance company might call the practice to conduct a “peer-to-peer” (P2P) with a clinician. During a P2P, a clinician needs to explain why a patient needs a particular service. A similar process is required for pharmacy denials for off-formulary prescriptions (e.g., “Yes, this patient needs the Tier 3 medication because she had an allergic reaction to the Tier 1 preferred option.”).
  • Explaining Insurance Benefits: A patient does not understand his benefit design, and the practice staff needs to explain it to him, along with providing an out-of-pocket cost estimate for his high deductible health plan.



3. Document Management

A significant portion of clerical overhead is spent managing documents, including responding to medical records requests, triaging incoming documents, and completing medical and administrative forms.



  • Chart Extraction: A law firm requests medical records for a patient. Practice staff need to identify the patient and excise sensitive information from the medical records (e.g., mental health history). Then they need to mail or fax the records and generate an invoice.
  • Document Triage: During a 30 minute period, six specialist reports, two radiology reports, and twenty medication refill requests arrive via fax. Practice staff have to classify each document (CT scan or CT angiogram?), triage what’s time sensitive, and route each document to the correct patient’s chart, often by matching names and dates of birth. If a name or date of birth is inaccurate, they have to destroy the document, contact the sender, and request a new set of records to avoid a HIPAA violation. If the fax is illegible, they have to contact the sender to have the document re-sent.
  • Form Generation: A patient needs their PCP to complete a four-page FMLA form, a referral for FSA reimbursement, or a back to school form. Support staff take a pass at filling out the form, and pass it to the PCP to complete the clinical components.



In addition to lowering the cost of care, automating clerical aspects of physician practices would help reduce errors, speed up access to care, and shift our spending to higher-impact roles — reskilling medical billers as care navigators, home health aides, and health coaches. Moreover, there are countless clinically-focused AI opportunities, such as augmenting population health outreach, extracting relevant clinical data from medical records, and facilitating care coordination.


So, chatbots might be fun, but there’s probably a real business to be built automating the clerical operating system of the medical economy.