You'll gain a deeper understanding of EHR’s data demands and clinical intelligence limitations by understanding how NLP harmonizes clinical information, structured and unstructured.


1. © 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

2. - Webinar objectives
- Evaluating the current state
- Harmonizing clinical information
- Sample workflows
- Conclusions, questions and answers
2
Agenda:

3. 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.
3

4. Evaluating the current state
EHR proliferation: by the numbers
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4
CMS EHR Incentive Program. March 2015. http://www.cms.gov/Regulations-and-Guidance/Legislation/
EHRIncentivePrograms/Downloads/March2015_SummaryReport.pdf
HHS: 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

5. 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

6. Evaluating the current state
Patient 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

7. “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

8. 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

9. 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

10. “We can’t solve problems by using
the same kind of thinking we used
when we created them.”
– Albert Einstein

11. Harmonizing clinical information
Harmonize
: 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

12. 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?

13. NLP – the hub or the spoke?
DATA
REPOSITORY
RULES
ENGINE
PreP
Code
NLP
CDI
RSEARC
H
CQM
RESEARCH

14. Understanding the ABC’s of NLP
Why 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

15. Solves healthcare leadership debates

16. Understanding the ABC’s of NLP
How 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"/>

17. 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

18. - Webinar objectives
- Evaluating the current state
- Harmonizing clinical information
- Sample workflows
- Conclusions, questions and answers
18
Agenda:

19. 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

20. Improves CDI outcomes without disrupting physician workflow
What 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. Albuterol
2. Insulin
3. Warfarin
LABS: Glucose 278, Bicarb 17, pH 7.25…
Assessment & Plan:
1. CHF w/ 30% EF, start on IV Lasix
2. Diabetes
3. COPD
4. Hypertension
✓ CHF NOS
✓ IV Lasix
✓ 30% EF
AcSyHF
Alert CDI
✓ T2DM
✓ Glucose >250
✓ Bicarb <18
✓ pH <7.3
✓ Fatigue
DKA Un
Alert 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. Albuterol
2. Insulin
3. Warfarin
LABS: Glucose 278, Bicarb 17, pH 7.25…
Assessment & Plan:
1. CHF w/ 30% EF, start on IV Lasix
2. Diabetes
3. COPD
4. 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. Albuterol
2. Insulin
3. Warfarin
LABS: Glucose 278, Bicarb 17, pH 7.25…
Assessment & Plan:
1. CHF w/ 30% EF, start on IV Lasix
2. Diabetes
3. COPD
4. Hypertension
Data-Driven Workflow

21. 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
Old
1:10
Greater CDI efficiency improves financial outcomes through increased review rates
What does it mean?
Jane Smith
Discharged
7 days ago
1. Clarify type of
CHF
Jack Smith
Admitted
2 days ago
1. Clarify type of
CHF
2. Poss’ DKA?
• Concurrent
• Reactive
• Electronic
Processes
• Better Impact
Current
3:10
• Instant
• Proactive
• Automated
Processes
• High Impact
Next
8:10

22. 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

23. Another Case in Point
Shriners 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

24. Sample workflows in HIM: Auditing
CAC 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

25. 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

26. 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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27. Questions, Answers and Discussion
Steven Bonney
EVP, Business Development & Strategy
RecordsOne | 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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