Basic “Algorithms” for US Healthcare

John Odden, Collaborative For Universal Health (C4UH)

ABSTRACT

US healthcare is generating costs (spending) that may threaten the viability of the national economy. 

$$$ Billions have been invested to migrate most of the US healthcare providers to digital patient records. The resulting “Certified EHR” vendors are said by some to operate “walled gardens.” These oases may not readily absorb external information and may not effectively share a patient chart with external sites. 

Adding in missing information, rules or algorithms is often reported to be difficult or unaffordable. 

Can C4UH, a small 501C3 entity, possibly make a contribution? 
Our hypothesis: Yes, assuming simple use cases facilitated by Mathematica!

Observations

Modern Health IT  and Health Analytics face a multitude of challenges.  We have guidance:

“The successful scientist must know when to discard extraneous results.”
	Richard Feynman, Nobel Laureate, shared in “Physics X” circa 1973 in reference to the Banach-Tarski 		 paradox (aka the pea and the Sun paradox)
	
“What man sees depends both upon what he looks at and also upon what his previous visual-conception experience has taught him to see.”
	Thomas S. Kuhn, The Structure Of Scientific Revolutions University of Chicago Press; 
	  Fourth edition (April 30, 2012)

“The problem is never how to get new, innovative thoughts into your mind, but how the get the old ones out.”
	Dee Hock, founder and former CEO of Visa, the credit card association 

“The Fortune at the Bottom of the Pyramid:  Eradicating Poverty Through Profits”
	Prof. C. K. Prahalad’s chosen title for his 2004 landmark book

Use Case 1 - Clinical Practice Guidelines

What is a Clinical Practice Guideline (CPG)?  
	A CPG describes rule-ins, rule-outs, treatment options and care steps 	for a 		 specific condition / disease / diagnosis
How useful is a CDF expressing a CPG as a basic algorithm?
	A dozen CPGs may lift hospital and physician income by a double-digit 			percentage under accountable care or value-based care
Each CPG may have roughly 12-48 selectable text blocks
	Selecting 6 to 12 CPG text blocks replaces typing from 500 to 1,500 				 characters into a patient encounter note
Physicians trained at differential diagnosis and the CPGs provide the “computation,” using the CDF in an encounter room PC when it is  idle  
	A CDF marshalls their selections to paste into the Certified EHR patient 			 encounter note

CPG Deployment - Innovative Approach

Use a CDF to invoke / track CPGs outside the scope of Certified EHRs

External consumption and tracking of CPGs avoids costly redeployment of currently installed Certified EHRs.
	(A single health plan contract requiring specific CPGs may span multiple 			provider entities)
	(Each provider entity in a metro service area may treat patients covered by 			multiple health plan contracts, each mandating different sets of CPGs)

Specific CPG use recorded in the patient encounter note of each relevant Certified EHR assures consistent adoption and reporting of contracted standards of care.
	(CPG consumption can be tracked longitudinally per patient across providers)

CPG Deployment - Legacy Option

“Traditional” encoding within each Certified EHR

CPGs “scripted within an EHR” are accessible to every licensed EHR user
	(EHR user licenses can run >$500/month/user due to amortized costs)
	(a health system may include 1,000’s of physcians using many Certified EHRs)

EHR Certification does not embrace CPGs as a structure or concept
	(value-based and accountable care emerged after Certified EHR adoption)

EHR reporting has no standard for reporting CPG consumption
	(EHRs may have varying data structures, even the same EHR brand may be 			customized to 	present hundreds of unique reporting patterns)

Can CDF actually support CPGs?

A simple UI handles designating the physician and choosing the CPG
CPG selections (typically 6 to 12 items) supplants a 500 - 1,500 keystroke text note

The CPG selection is pasted into the EHR encounter note window, previously set by the assistant

A Wolfram Databin (or other store) can track physician, timestamp, CPG and selections as a simple table, no PHI or PII involved

Use Case 2 - the “CMS AI Challenge”

CMS, the HHS agency managing Medicare and Medicaid seeks to showcase explainable AI solutions that help front-line clinicians and patients:
 


C4UH proposed to predict failure of medication assisted treatment for OUD

OUD & Medication Assisted Treatment - Status

OUD - Opioid Use Disorder - the opioid crisis is a public health emergency

Medication Assisted Treatment (MAT) is a proven regime for recovery

We hear that the only accepted risk measure for MAT success/failure is NOT appearing on a coroners’ mortality report (e.g. patient is not dead yet)

C4UH makes a proposal:

	MATfailureRisk = Predict[CMSaiLimitedDataSet]

	MyMATrisk = MATfailureRisk[myClaims]

Implications of C4UH’s Proposal

Find out if the approach works based on the offered Limited Data Set (LDS):

	MATfailureRisk = Predict[CMSaiLimitedDataSet]

If successful, then it’s useful to know risk (without use of Mortality Report)
	NOTE:  The LDS is “90 day settled claims” = 90 day latency
	
C4UH mentioned a “parallel path” -
	Build a similar model, adding Social Determinants Of Health (SDOH)
	
	Why use SDOH to augment 90 day settled claims?
	
	SDOH can be obtained with 24 to 72 hour latency

Stepping Back - Solution Priorities

90 day settled claims are a standard for rate setting, quality measurement, and much of population health management

Patients with serious chronic issues may be measured retrospectively with best accuracy and integrity with 90 day settled claims

However, allocation of attention to current cases is often best guided by low latency predictive measures, thus the C4UH proposal:

	Validate with a Limited Data Set
	
	Attempt to replicate with low latency SDOH

Questions