The Value of the Clinical

Microbiology Lab in Today’s Changing Healthcare Environment

Karen Kaul, M.D., Ph.D.

Chair, Pathology/ Lab Medicine NorthShore University HealthSystem

Clinical Professor of Pathology University of Chicago Pritzker School of Medicine

New Financial Realities in Healthcare

• US government funds > 50% of our nation’s $3.8 trillion healthcare expenditures

• Lab testing: >$80 billion – 3% of health care cost (1.5% of Medicare) – dictates 70% of downstream spend

• Lab must reduce cost, increase quality and speed, improve overall outcome and cost

Quality

Cost

Value =

New Government Regulations: PAMA – Protecting Access to Medicare Act

• New ‘market-based’ payment rates for lab tests

• Data collection from labs • 10% decreases to Lab Fee

Schedule year over year for 3 years with another 15% decrease years 4-6

• Commercial payer following suit

• Will move labs from revenue center to cost center

Lab’s role in Care Transformation

• Reduce waste, unneeded testing • Use of appropriate testing • Faster, more valuable results • Coordinate lab tests across spectrum of care

– Inpatient, outpatient, outreach • Be more integrated, more available to care team • Create IT solutions in physician workflow

Labs are well-positioned to influence cost and quality

Increasing Lab Value

• Appropriate Laboratory Utilization • Laboratory Consultation • Automation/lab efficiency • New approaches to diagnosis • Integration into hospital quality programs • Integration / use of IT and EMR • Track contributions: value based system

Lab utilization improvement • Right test at the right time

– Clinician understanding of 50-100 tests – Strongest predictor of lab order patterns is residency

• Nomenclature confusing • Technology evolving quickly • Tests over-ordered? under-ordered? Who orders? • The curse of the order sets! • Interpretive guidance (today’s docs are less prepared

to use the lab properly)

Utilization issue in Microbiology

• Blood cultures – >2 sets, fill volume

• Urine Cultures – Reflex from UAs? – Asymptomatic bacteruria

• Stool – O&Ps – Cdiff on formed stool

• CSF workups – Auto-ordering of large infectious panels

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Clinical case rounds in micro lab: team consultation

Grand rounds, teaching rounds, diagnostic management teams…

Increasing consultation and communication

• Clinicians live in electronic medical record • Electronic communications prevail • Information at fingertips

• Clinicians

– Don’t know lab – Don’t know Pathologists – Don’t take time/don’t have time – Life measured in clicks – Now working in systems

MyPathologist

• Purpose • "MyPathologist" provides a

direct connection between clinicians, and Pathologists and Lab Scientists to answer questions such as: – Should a test be ordered? – Which test should be

ordered? – How should this test result

be interpreted? – Is follow-up testing needed? – I need clarification of a report

• Location

Generates Electronic Message Message is prepopulated to lead user through the process of receiving help. Text page to CP resident (smart phone)

Theparee et al, Academic Pathology 2018.

Technologic advances (faster, better, cheaper?)

• Dramatic advances in microbiology! • Specialized media • Automation • Molecular • MALDI • The need for speed • Appreciation of infection control needs

Diagnosis of infection and antimicrobial resistance

H1N1

H3N2

Swine-derived Pandemic

no templa

te

Kaul et al, J Molecular Diagnostics, 2010

Real time PCR

Conventional

Molecular and MALDI

Molecular diagnosis of MRSA sepsis Impact on Time to Result/Report and Antibiotic Use

1.9 h (report to Pharmacist)

1.9 h (total 10-17h)

24.9 h

8-15 h

Colony /PCR/ Liquid culture/PCR

15.7 h (total 58.2h)

42.5 h Colony/ Culture susceptibility

Time from Report to Antibiotic Change

Time to Susceptibility Report Specimen/Test / Method

Earlier diagnosis -> better outcomes, improved antibiotic use!

Rapid panels and POC PCR

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Benirschke, McElvania, Thomson, Kaul and Das. Clinical Impact of Rapid POC PCR Influenza testing in the urgent care setting. JCM 57: e01281-18, January 2019.

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Lab automation

Value of new technologies/automation

• Increased reproducibility • Aids workforce issues • Continuous work schedule, efficiency • May lower cost • Faster results

– Continual incubation – Molecular and MALDI detection – Faster diagnosis and treatment – Better clinical outcomes!

Continuous incubation

MALDI and molecular ID

Automated inoculation

21 | AAU Study Highlights | NorthShore | April 21, 2010

New Technology v Conventional Methods

Collection Incubation 1st “Read” 2nd “Read” Inoculation Identification 80% Identification 20% Antimicrobial Result

0 2 3 16-18 18-20 42 44 h

Collection Incubation 1st Image Inoculation Identification 100% Antimicrobial Result

0 1.5 2 10 11h 30 h

23 | AAU Study Highlights | NorthShore | April 21, 2010

Significant impact on TAT, LOS Time to ID cut by ~5 hours (23%) (now 8 h) $250K impact on cost outside lab (LOS) Tests performed/FTE increased 14% 6 FTEs ~$500K annual savings Better clinical outcomes!!

- 6% of 35 million annual admissions (2.1 million patients) develop HAI - 4th leading cause of death in U.S.; 103,000 died in the year 2000 - Most are preventable - Spending on infection control saves lives and reduces hospital stays Healthcare-Associated Infection, 2002

Treat infection, or prevent it?

Carriage of resistant organisms • MRSA, C difficile, VRE,

KPC, MDRO • Screen to identify carriers • Implement precautions

– Prevent spread • Decontamination

– Prevent infection • Improve clinical outcomes • Reduce unreimbursed cost

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Medical and Economic Outcome • Excess expense of MRSA infection (compared

to no infection) = $24,000 • Cost of lab program under $150,000/yr • The first 10 years of NorthShore MRSA

containment program prevented 1,000 infections – Net direct benefit from medical expense reduction is

over $20 million ($2M/Year) – Number of deaths avoided = 180 (18/Year)

LR Peterson. JCM 48:683-9, 2010 LR Peterson et al. Jt Comm J Qual Patient Saf 33:732-8, 2007 RM Klevens et al. JAMA 298:1763-71, 2007

Rate of MRSA Nosocomial Infection

LR Peterson and DM Schora J Clin Micro 54:2647-2654, 2016

Total infections per 10,000 patient days

MRSA Screening Begins

EMR embedded tools

• Automatic ordering of screening PCR • Positives get isolation cart auto-sent to floor • Flag on positive patient chart in EMR • EMR-embedded risk tool since 2012

• Admission screening for C diff • Extended screening for MDROs in ICUs

Predictive Modeling for Risk-Based Testing • Our admission MRSA prevalence dropped

from 10-12 % to <2% over 10 years. • NS developed an EMR-embedded prediction

rule that reduces need for testing • Age, history, home vs nursing home, clinical

problems, other factors • Developed and validated in 25K cohorts • Reduced MRSA admission screening by half (Get credit for the $ saved!)

A Robicsek et al. ICHE 32:9-19, 2011

New Focus: Nursing home partners

• Long-term care facilities harbor MDROs! • Outbreak of KPC-producing

enterobacteriaceae in Chicago in 2010 • Developed PCR screening test for rectal

swabs (KPC, CTXm, NDM, IMP, VIM) – JCM 51:3423-25, 2013

• 70% of long-term care patients colonized – PCR more sensitive and faster than chromogenic – JCM 50:2596-2600, 2012

• Reach out to partner with nursing homes! (episodes of care, readmission rates)

Antibiotic stewardship

• 50% of inpatients get antimicrobials – Half don’t need it

• Cost, collateral issues • Increasing antimicrobial resistance • No new drugs in pipeline • CDC, WHO, IDSA on board • Mandate from JCAHO January 2017 • CMS mandate

Antibiotic stewardship: team effort

• Education of prescribers, nurses, patients • Monitoring and reporting of antibiotic use • At NS, ID docs review all new antibiotic

orders daily – EMR list generated chart review – Place recommendation in chart or page doc

• Also correlating lab results with antibiotic choice

Antibiotic stewardship - RESULTS

• 25% recommend antibiotics be cancelled • 25% recommend antibiotics be changed • 85% of recommendations accepted • Monitor actions, Rx days/1000 patients • Improved quality and outcomes • Reduced expenditures, cost

48-hour Antibiotic Use Best Practices Alert

Lab is primary source of medical information

• All patients get lab tests • 70% of data in EMR is lab-derived • What to do with that data?

EMR/IT and lab partnership: Put data in hands of physicians!

• Mountains of data in EMR! • Need to move from “its in there

somewhere” to “just in time” (data at point of need)

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Lab-driven predictive analytics Electronic Cardiac Arrest Risk Triage (eCART)

Age, years Number of ICU stays Respiratory rate, bpm

Heart rate, bpm Systolic bp, mm Hg Diastolic bp, mm Hg

Temperature, degrees C Pulse pressure index Oxygen saturation, % Mental status (AVPU)

Sodium, mEq/L Potassium, mEq/L

Alkaline phosphatase, U/L

Multicenter Development and Validation of a Risk Stratification Tool for Ward Patients Churpek MM, Yuen TC, Winslow C, et al. Am J Resp Crit Care Med 2014;190:649-55

Bicarbonate, mEq/L Anion gap, mEq/L

BUN, mg/dL Creatinine, mg/dL

BUN-creatinine ratio Glucose, mg/dL Calcium, mg/L

WBC, K/μL Hemoglobin, g/dL

Platelets, K/μL Total protein, g/dL

Albumin, g/dL Total bilirubin, mg/dL

AST, U/L

Exis

ting

Med

ical

risk

indi

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Use the data: predictive analytics

Other predictive analytics efforts

• Sepsis • Readmission • Diabetes

The future of precision medicine: the Microbiome

• Complex host/ microbiome/ environmental interactions • Normal microbiome important in health and infection • Microbiome important in disease risk

– Inflammatory bowel disease – Diabetes – Obesity – Cancer – Preterm labor – Cancer treatment response

• Studied by deep NGS sequencing of 16S, other genes

42s

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Maturation of the gut microbiome and risk of asthma in childhood Stokholm et al, Nature Comm. 2018 DOI:10.1038/s41467-017-02573-2

Asthma risk increases: - Caesarian section - Intrapartum antibiotics - Antibiotics in 1st year - Maternal history Newborn gut microbiome: - Matures - Different organisms - Diversity matters! - Composition of gut microbiome at 1 yr predicts asthma

Maturation of the gut microbiome and risk of asthma in childhood

Stokholm et al, Nature Comm. 2018 DOI:10.1038/s41467-017-02573-2

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THANK YOU!