Applications of aggregate data in healthcare and

research

Mark Hoffman, Ph.D.

Chief Research Information Officer

@markhoffmankc

Topics

• Background

• Genomic consent

• Public data sets• Envirome

• Data derived from Electronic Health Records• Disease surveillance• Research• Quality Improvement• HIE data

• Big data required to train AI / Machine Learning algorithms

Data explosion - Biology and Medicine

YOU!Every search

Every like

DevicesConnectome

• 1010 neurons• 1014 connections

Genome

• 20,000 genes• 3 Billion nucleotides from each parent

Microbiome

• 10 trillion cells• 10x > than our own cells

Proteome

• 250,000 – 1M unique proteins

Apps

EHR

• VOLUME

• Velocity

• V a r i e t y

• Veracity

Large loads, no formal number as threshold

Data in motion, not at rest

Data has complexity

Data can be traced to origin, point in time

Characteristics of “Big Data”:

Veracity: Relationship between “Big Data” and “Little Data”

Irresponsible data: Questionable Veracity at the “Little data” level• “Big data” analysts don’t understand “little data”• Failure to recognize inherent bias

• Race, gender, age, socioeconomic, regional

• Unknown origin (provenance)• Out of context• Inappropriate level of rigor• Hype• Disallowed access

Whole exome sequencing (WES)

• Increasingly utilized for diagnostic purposes

• Sequence every gene

• Secondary findings – risk factors unrelated to original reason for ordering test

• Presidential Bioethics Commission and American College of Medical Genetics have specific recommendations for consent WES consent forms

Consent elements matrix

• Download consent forms from 18 academic and commercial labs offering diagnostic WES

• Score each form for recommended elements

• Also evaluated grade level readability of formo Flesch-KincaidoAverage was 10.8 vs

recommended 8th gradeo Some college reading level Fowler, SA; Saunders, CJ; Hoffman, MA “Variation among consent forms for

clinical whole exome sequencing” J. Genetic Counseling. July 2017 ePub. PMID:

28689263

Consent content

• All labs acknowledge uncertainty of results

• Most acknowledge possible inclusion of data in databases

• Few labs discuss release of secondary findings to family

Fowler, SA; Saunders, CJ; Hoffman, MA “Variation among consent forms for

clinical whole exome sequencing” J. Genetic Counseling. July 2017 ePub. PMID:

28689263

Envirome Data

Envirome data service

Zip codeCensus tract

ContextData

ElectronicHealth Record

ResearchData set

2010 Census

USDA Food Desert

Pu

blic

Dat

a So

urc

es

Address

Geocode

GeocodingService or

Application

Covered Entity

GIS installed in

house

Geocoding – Strategies for protecting PHI

BAA?

GeocodingService

Provider

Distinct IP of origin

If research, include in consent

BAA

Cloud ServicesProvider

• Address is one of 18 HIPAA protected fields

Live at Children’s Mercy

Source and version clearly presented

Precision to enable reproducibility

Primary uses of EHR data

• Support point of care decisions

• Enable immediate access to documentation

• Promote compliance

• Protect patient privacy

• Automate and streamline clinical operations

• Billing

Disease Surveillance – Public Health

• Some pathogens require notification of public health• Highly contagious

• Food poisoning

• Bioterrorism

• Requirements vary by jurisdiction

• Historically notification was by FAX, mail or phone call

• Electronic reporting directly from EHR offers multiple benefits

2001 - Anthrax

• Anthrax contaminated letters sent to news media and U.S. Senators

• 5 fatalities, 17 infections

• Kansas City Health Department and Cerner agreed to collaborate

Public Health

Surveillance Architecture

DATA COMPLETENESSReportable cases (non-STD): March-Sept 2002

*Average over 6 key data fields

UNDER-REPORTING

0%

100%

200%

300%

400%

Campy

SalmShig

Grp A

Stre

p

H. Influ

Hep C

% I

nc

rea

se

0%

20%

40%

60%

80%

100%

Conventional HealthSentry

0.0

0.5

1.0

1.5

2.0

2.5

3.0

Conventional HealthSentry

TIMELINESS

% F

ield

s C

om

ple

te

*Average over all reportables

*Increased overall reporting by 96%D

ays t

o r

ec

eiv

e r

ep

ort

Improved public health reporting

Hoffman, MA., Wilkinson, T, Bush, A, Myers, W, Griffin R, Hoff, G, Archer, R. “Multijurisdictional approach to Biosurveillance, Kansas City” Emerg. Inf. Dis. 2003 9(10):1281-1286 PMID: 14609464

Public Health Network: 2009 Influenza initiative

• Opt-in at project level

• 850+ facilities, 48 States

• 57 million cases processed

• Positive influenza A results, ILI, ED

utilization

• Worked with CDC, state and local public

health

Data waterfalls

• Every handoff can result in the loss of meaning

• Data attribution

• Need to understand workflow and data flow at all levels and transitions

Intermediate 1

Source

Intermediate 2

Health Facts

No

data righ

ts

EHR Vendor clients

Health Facts™

De-ID Mapping, normalization

Data righ

ts

Health Facts data waterfall

• PHI

• Text documents

• Images

Data extracts

Electronic Health Record

Data warehouse

• Duplicates

• Cancelled orders

Health Facts

Data type Current release

Unique patients 63 million

Total laboratory results 4.3 billion

Total facilities 863

Total medication orders 734 million

Total diagnoses 489 million

Cerner Health Facts - Summary

• Actual, not potential data

Can we validate large EHR data sets?

Diagnostic category HCUP NIS Health Facts t value relative difference

Nervous system 6.03 6.12 0.39

Eye .15 .14 1.53

Hepatobiliary and pancreas 2.94 3.03 1.02

Male reproductive .5 .55 2.23

Female reproductive 1.75 .55 24.04

Pregnancy and childbirth 11.09 4.15 18.67

Myeloproliferative, poorly differentiated neoplasms

.91 .86 0.7

Mental health 3.89 2.22 7.1

Trauma .27 .21 3.08

DeShazo, J; Hoffman, MA “A comparison of a multistate inpatient EHR database to the HCUP nationwide inpatient sample” BMC Health Services Res. 2015 15(1):384 PMID: 26373538

Mg and AMI - Mortality

• Mg supplementation recommended after AMI but little evidence

• After inclusion/exclusion –11,683 HF patients with AMI and Mg results

• Both Low and High Mg levels correlate with higher risk of in-hospital mortality

Shafiq et.al. – J. Amer. Coll. Card. June 2017

Let the data speak

• Risk factors associated with hospital acquired C. diff infections

• Regression analysis

• Does not require a narrow question

Dean B., Campbell R., Nathanson B. et. al. “Risk factors associated with hospital-origin vs community-origin Clostridium difficile-associated diarrhea” ID week 2012

Data-informed selection of QI projects

A1c / Sickle Cell patients -Comparison of TMC to all HF sites

392711%

3322489%

Sickle Cell Patients at all 393 Facilities

Atleast one A1C Encounters No A1c Encunters

17032%

35668%

A1C Encounters of Sickle Cell Patients at TMC

Atleast one A1C Encounters No A1c Encunters

• Confirms high frequency

HIE Data

• Health information exchanges (HIE) are implemented to support patient care

• Most HIE agreements do not provide clauses that establish policies and procedures for the research use of the unconsented data (for research) crossing through the HIE

• HIE operator should not offer to redistribute or provide access to data for research without full understanding of consent policies

• Very risky territory, especially in light of Cambridge Analytica / Facebook issues

Recent developments

Why we need algorithms…

• Limits to human memory• 3,874 genes with phenotype-causing

mutation (OMIM)

• Limits to human perception• Vision limit: 170 pixels per inch• Limits of hearing, touch

• Limits to our ability to recognize patterns• Subtle but large scale variations

across population

• Knowledge explosion• “Omics”• 23 million citations in MedLine

• Major opportunities to improve the quality of care• Non-adherence to guidelines• Hospital acquired infections• Adverse events• Health disparities

• Efficiency• Workforce shortage –

• 53.3 physicians / 100,000 people –Urban

• 39.8 physicians / 100,000 people –Rural + Much greater distances

• High cost of healthcare

Algorithms don’t just happen…

Data!

Supervised learningUnsupervised learning

Deep learningAlgorithm

Algorithm

Literature reviewEvidence-based

medicineHuman curation

Randomized clinical trials

Providers apply algorithms based on this model:

Algorithms are hungry for data

• Supervised learning – heavy analyst involvement. For example, annotation of pneumonia cases by radiologist.

• Unsupervised learning – cluster analysis.

• Deep learning – algorithms train algorithms – unstructured, unlabeled data Training

setExperimental

Total data set

Most data must be “cleansed” before feeding algorithm

The reality… there is often a “man behind the curtain”

What we want to imagine: a well oiled data machine

Most data must be manipulated before feeding algorithm• Standard data prep:

• Convert text to numeric

• Address missing values

• Address values that are “out of bounds”

• This is often perfectly appropriate but requires subject matter expertise

Caucasian Female

Abdominal Pain,

Unspecified Site

Acute Bronchitis

Acute Pancreatitis

Benign Essential

Hypertension

Coronary Atheroscleros

is of Unspecified

Type of Vessel

Diabetes Mellitus without

Mention of Complication,

Type I

Diabetes mellitus without

mention of complication,

type II or unspecified

type

Diarrhea

Dysuria

Esophageal Reflux

Fever, UnspecifiedHypersplenis

m

Hypopotassemia

Nausea with Vomiting

Lymphosarcomaand

Reticulosarcomaand Other Specified

Malignant Tumors of

Lymphatic Tissue

Other Nonspecific Abnormal

Serum Enzyme Levels

Personal History of

Other Diseases of Digestive System

Q Fever

Thrombocytopenia,

Unspecified

Unspecified Chronic

Bronchitis

Unspecified Idiopathic Peripheral

Neuropathy

Urinary Tract Infection, Site Not Specified

Lymphosarcoma

Type II Diabetes

Q Fever

One very ill woman

Peripheral Neuropathy

Pancreatitis

Patient type categories (subset)

Code Category

77 Client

78 Clinic

76 Cerner test patient – not valid patients

122 HLA QC

123 Home health

109 TestUpdate: Cerner has removed manyNon-patient encounters in latest HF data cut

What can we do? Implementation Science• Data science teams must be interdisciplinary – subject matter and

technical• Contribute as clinical experts – you don’t have to be a programmer!

• Reduce data issues upstream of AI and algorithms• Advocate best practices for system implementations:

• Automate “boundary” checking for values:• Expected range• If necessary, provide override

• Thoughtful consideration of default values• Zero vs Null

• Versioning• Clinical forms – add or remove prompts

Thank you!

• mhoffman@cmh.edu

• @markhoffmankc

• My blog: markhoffmankc.com

• Some work funded by Centers for Disease Control and Prevention• Grant NU47OE000105-01-01

• Thanks to:• Kamani Lankachandra – UMKC/TMC

• Suman Sahil - UMKC

• Shivani Sivisankar - UMKC

• Earl Glynn

• Children’s Mercy Research Informatics Team