© 2015

Building Data Driven Workflows in HIM: More than just an EHR

Steve Bonney, EVP, Business Development & Strategy

RecordsOne Solutions for CARe: Collaboration, Analytics & Reimbursement

- Webinar objectives - Evaluating the current state - Harmonizing clinical information- Sample workflows- Conclusions, questions and answers

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Agenda:

Objectives:

- Gain a deeper understanding of EHR’s data demands and clinical intelligence limitations.- Understand how NLP harmonizes clinical information, structured and unstructured. - Discuss sample HIM workflows using NLP.

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Evaluating the current stateEHR proliferation: by the numbers

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CMS EHR Incentive Program. March 2015. http://www.cms.gov/Regulations-and-Guidance/Legislation/EHRIncentivePrograms/Downloads/March2015_SummaryReport.pdfHHS: News. http://www.hhs.gov/news/press/2014pres/12/20141205a.html

• Registered EHRs: 4,811 hospitals / 530,756 eligible providers

• 80% of physicians have them!

• Healthcare providers that have received payments: 447,000

• Top reasons to implement: financial incentives, ability to

exchange

EHR Success, Or Not• Pre and Post-Payment Meaningful Use Audits: Average of

17% failed

• Multiple issues remain

– Cut and paste issues

– Data integrity issues

– Paper progress notes

– CPOE still not implemented

– Physicians are frustrated

– Interoperability kludgey at best, non-existent at worst

Evaluating the current statePatient data explosion: but problems remain

• Top problem: combining different types of data from different sources – paper charts - dictation -HL7 2.x messages -EHR text

• Critical issue: managing volumes of data effectively

• Key areas of concern: data capture, storage and processing

• Increased spending: average hospital will spend $1.9 on analytics in 2015

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CDW/O’Keefe Survey: Analytics in Healthcare: http://www.cdwnewsroom.com/wp-content/uploads/2016/01/CDW_Healthcare-Analytics-PR-Report_FINAL.pdf

“In general, 20 percent of EMR data is structured and 80 percent is unstructured. While it's easier to mine structured data, such as medications, the "golden nuggets" of information, such as ejection fraction, are often hidden away in an unstructured format in clinical notes. The problem is that traditional data analytics tools—aggregate views and trend reporting—don't work with unstructured data.”

http://www.cmio.net/index.php?option=com_articles&view=article&id=34125:nlp-tackles-unstructured-data

Evaluating the current state Healthcare needs structured data: but unstructured remains

Operational Problems in HIM• Understanding what the “text of the message” contains:

EHRs can’t do it • Managing what paper remains

• Reviewing and analyzing the entire record

• Hybrid chart impacts: low productivity, high cost • Automating workflows in:

– Coding

– CDI – Quality reporting

• Risk-based Auditing

Technology Problems in HIM

Layering vs. Harmonization• Layer products / software applications on top of the EHR

• Separate products vs. modular approach to HIM

– encoder, CDI, analysis, etc. • Platform approach: harmonization of technology

• Modules are “turned on” or added to single database

– Different users with varying needs all use same pool of data

– Everyone who needs the database information , can get access to it

“We can’t solve problems by using the same kind of thinking we used

when we created them.” 

– Albert Einstein

Harmonizing clinical informationHarmonize : to be combined or go together in a pleasing way : to be in harmony: to cause (two or more things) to be combined or to go together in a pleasing or effective way

NLP – the question or the answer?• Vendor confusion

– NLP, NLU or CLU?

• What are the impacts

– CAC? CDI? – CQM? MU? ACO?

• Clinical vs. billable

– allergies, immunizations, labs…

• Is NLP in my EHR?

• Is NLP the cure all?

• Is NLP right for me?

NLP?

NLP – the hub or the spoke?

DATAREPOSITORY

RULESENGINE

PreP CodeNLP CDI

RSEARCH

CQM

RESEARCH

Understanding the ABC’s of NLPWhy it’s important• NLP is the mechanism for creating data

• Harmonizes data to analyze performance, quantify organizational impact – ACOs

– P4P

– VBP

– Quality measures

– And more

• For example: – identifies high risk patients before they

become patients

Solves healthcare leadership debates

Understanding the ABC’s of NLPHow it works• Rules determine how engine works

• Turns words into action

• Harmonizes well-organized patient information

– coded

– searchable

– reportable

– actionable

– Interoperable

• Shifts case review to “risk-based”

<section c="report chief complaint item"> <structured form="xml"> <problem v="chest pain" code="SNM:29857009_pain chest" idref="p13"> <IMO CERTAINTY="exact" DOMAIN="ProblemIT" ICD9_LEXICALS_TEXT_IMO_CODE="85191" LEXICAL_TITLE="Chest pain" ICD9CM_CODE="786.50" ICD9CM_TITLE="Chest pain, unspecified" ICD10CM_CODE="R07.9" ICD10CM_TITLE="Chest pain, unspecified" SCT_ID="29857009" SCT_TITLE="Chest pain"/> <parsemode v="mode1"/><sectname v="report chief complaint item"/> <sid idref="s2"/><code v="SNM:29857009_pain chest" idref="p13"/> </problem></structured><tt></tt></section><section c="report history of present illness item"> <structured form="xml"><finding v="demo"><age v="37 year" idref="p36"/> <parsemode v="mode1"/><sectname v="report history of present illness item"/> <sex v="female" idref="p42"/> <sid idref="s4"/> </finding><problem v="gastroesophageal reflux" code="SNM:54856001_ gastroesophageal reflux disease!SNM:54856001_gastrooesophageal reflux disease" idref="p56"><IMO CERTAINTY="exact" DOMAIN="ProblemIT” ICD9_LEXICALS_TEXT_IMO_CODE="44649" LEXICAL_TITLE="Gastroesophageal reflux" ICD9CM_CODE="530.81" ICD9CM_TITLE="Esophageal reflux" ICD10CM_CODE="K21.9" ICD10CM_TITLE="Gastro-Esophageal Reflux Disease Without Esophagitis" SCT_ID="235595009" SCT_TITLE="Gastroesophageal reflux disease"/><duration v="2 year" idref="p60"/>

Digging into records vs. mining data• NLP rules evaluate content of record

for a specific purpose

• Results sent to human for review, decision making, intervention

• For example: – NLP determines which cases must

be reviewed

– NLP prioritizes cases (which should be reviewed first)

– Workflow routes list to correct human

- Webinar objectives - Evaluating the current state - Harmonizing clinical information- Sample workflows- Conclusions, questions and answers

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Agenda:

Sample workflows in HIM: CDI

Remote CDI program at Baystate Health• 3 hospitals, 1 EHR

• Data creates opportunities for CDIS analysis

– Lack of specificity

– Clinical evidence without diagnosis

– Clinical diagnosis without supporting evidence

• Organizational benefits

– Offset shortage of qualified CDIS staff – Bridge complexity gap

– Improve query rates

– Spend more time fixing documentation, not searching for cases

Improves CDI outcomes without disrupting physician workflowWhat are the impacts?

H&P for Burnt Orange Complaint: SOB, Chest Pain

HPI: Mr. Orange is an 82 YO Male with history of CHF who presents with shortness of breath, dizziness, fatigue and nausea...

PMHx: COPD, Prostate Cancer.SHx: Former smoker of 50 years.

MEDS: 1. Albuterol2. Insulin3. Warfarin

LABS: Glucose 278, Bicarb 17, pH 7.25…

Assessment & Plan:1. CHF w/ 30% EF, start on IV Lasix2. Diabetes 3. COPD4. Hypertension

✓ CHF NOS✓ IV Lasix✓ 30% EF

AcSyHF

Alert CDI

✓ T2DM✓ Glucose >250✓ Bicarb <18✓ pH <7.3✓ Fatigue

DKA UnAlert CDI

H&P for Burnt Orange Complaint: SOB, Chest Pain

HPI: Mr. Orange is an 82 YO Male with history of CHF who presents with shortness of breath, dizziness, fatigue and nausea...

PMHx: COPD, Prostate Cancer.SHx: Former smoker of 50 years.

MEDS: 1. Albuterol2. Insulin3. Warfarin

LABS: Glucose 278, Bicarb 17, pH 7.25…

Assessment & Plan:1. CHF w/ 30% EF, start on IV Lasix 2. Diabetes 3. COPD4. Hypertension

H&P for Burnt Orange Complaint: SOB, Chest Pain

HPI: Mr. Orange is an 82 YO Male with history of CHF who presents with shortness of breath, dizziness, fatigue and nausea...

PMHx: COPD, Prostate Cancer.SHx: Former smoker of 50 years.

MEDS: 1. Albuterol2. Insulin3. Warfarin

LABS: Glucose 278, Bicarb 17, pH 7.25…

Assessment & Plan:1. CHF w/ 30% EF, start on IV Lasix2. Diabetes 3. COPD4. Hypertension

Data-Driven Workflow

Joe Smith Just Admitted Room 123

1. Clarify type of CHF

2. Poss’ DKA?3. COPD Trial?4. Pop’ Health?

• Retrospect’• Manual

Processes• Highly

disruptive• Low Impact

Old1:10

Greater CDI efficiency improves financial outcomes through increased review rates

What does it mean?

Jane Smith Discharged7 days ago

1. Clarify type of CHF

Jack Smith Admitted2 days ago

1. Clarify type of CHF

2. Poss’ DKA?

• Concurrent• Reactive• Electronic

Processes• Better Impact

Current3:10

• Instant• Proactive• Automated

Processes• High Impact

Next8:10

Another case in point: Top ten IDN• 17 hospitals, 4 states, 2 EHRs

• Ability to analyze clinical documentation from each system, facility by facility

• Make CDI findings available via the web, remote

• Allow them to perform CDI for smaller hospitals w/out sending a CDIS

• Future state: pull documentation across facilities together and review all for broader decision making

Another Case in PointShriners Hospitals– 20+ facilities

– Preparing for ICD-10 • Focused case selection at each location

• Targeted physician education

• Improve documentation specificity

– Scoliosis

– Cerebral Palsy

– Cleft Palate

– Burn Injury

Sample workflows in HIM: AuditingCAC technology

• Movement from retrospective (training) to concurrent (risk-based) – Coders spend more time coding, less cccccc– Improve revenue, reduce loss– Reduce audit risk and recovery – Reduce employee time fighting RAC)

• Evaluate against PEPPER in real-time, pre-bill

Sample workflows in HIM: Quality Review Finding core measures patients• Quality reviewers spend more time reviewing cases, not

searching through charts

• Use NLP rules to assess documentation upon admission

- ID core measures patients as soon as they fail admission criteria in ED

• Use NLP rules to review progress notes, problems lists, etc. inhouse

– automatically identify cases, notify human for what is in the record

– Patient admitted with renal failure, Converts to CHF on day two

In Summary:

• The healthcare industry is data-driven and information-hungry.

• Despite the rapid proliferation of EHRs, significant gaps in clinical intelligence and information gathering remain.

• NLP helps establish data-driven workflows to: • Harmonize data • Support better decision making • Improve staff productivity • Make the most of your EHR data

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Questions, Answers and Discussion

Steven Bonney EVP, Business Development & StrategyRecordsOne | Solutions for CARe: Collaboration, Analytics & Reimbursement

mobile 410.703.3360 direct 239.208.0387 main  239.451.6112 Twitter @Records1_v6 Email steve@recordsone.com

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