1. Data Aggregation; The Forgotten Key to Analytics
October 26, 2017
Physician Community Webinar Series
#DrHIT @HIMSS

2. Welcome to the Physician Community Webinar Series Sponsored by the HIMSS Physician Community
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Covers a wide range of topics on Medical Informatics, HIEs (Health Information Exchange), Standards and Interoperability, eMeasures and Quality Initiatives, and how it affects, impacts and involves physicians.
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3. Welcome to the Physician Community Webinar Series Sponsored by the HIMSS Physician Committee
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A copy of the recording and slide set will be available for download within 5 business days on the Physician Community Webinar Series Archive Page

4. Moderator:
Joel J. Reich, MD Interim Chief Medical Officer Commonwealth Alliance HIMSS Physician Committee Member,

5. Speaker:
Joseph C. Nichols, MD, Principal, Health Data Consulting HIMSS Physician Committee Member,

6. Learning Objectives
Understand the role of data aggregation in the process of creating high quality information
Identify key challenges in the aggregation of medical concepts
Define the requirements for data aggregation that results in a shared understanding of any analysis.
Identify steps to apply a consistent process to transparently arrive at the right aggregation for the right purpose.
#DrHIT @HIMSS

7. Increasing Measures of Value
Source: Health Data Consulting
*Source: 7

8. The Focus is Changing
Diagnosis
Services

9. The Focus is Changing
Services
Diagnosis

10. Big Data
Is more garbage better? 10

11. Source: Health Data Consulting11

12. Information Quality 12 Observations Coded Data Aggregation Analysis
Accurate
Complete
Consistent Documentation*
Coded Data
Well-defined Standards
Accurate implementation
Robust Concept Support
Aggregation
Clear definition
Normalized
Accurate inclusion and exclusions
Analysis
Well defined
Logically valid
Consistently applied
Source: Health Data Consulting
Source: Health Data Consulting 12
Source: Health Data Consulting Inc.

13. Information Quality 13 Observations Coded Data Aggregation Analysis
Accurate
Complete
Consistent Documentation*
Coded Data
Well-defined Standards
Accurate implementation
Robust Concept Support
Aggregation
Clear definition
Normalized
Accurate inclusion and exclusions
Analysis
Well defined
Logically valid
Consistently applied
Source: Health Data Consulting
Source: Health Data Consulting 13
Source: Health Data Consulting Inc.

14. The Heart of Policies, Rules Edits and Analytics
Source: Health Data Consulting
Aggregation
The Heart of Policies, Rules Edits and Analytics

15. Aggregating Data Current Challenges
Most analytic tools leave it up to the user to define categorical disease parameters
Most users assume they know what the category means and that their understanding is the same as others
Categories and hierarchal relationships when defined are static
Drill-down to details must follow a predefined path
Medical concepts cross categories and do not fit in a single hierarchal bucket
Source: Health Data Consulting Inc.

16. Aggregating Data Limitations of current analytic tools
The content of categories is defined in a black box often by persons who lack a clinical background
Information is not actionable since questions about parameters of diseases are constrained to predefined static categories
Disease parameters cannot be combined
Source: Health Data Consulting Inc.

17. Aggregating Data Does it make sense?
Source: Health Data Consulting
Source: Health Data Consulting Inc.
Source: Health Data Consulting Inc.
MDMeta © 2016

18. Define: Aggregation Quality
Requirements
Define:
Shared understanding of concepts and intent
Precise and granular description of the category
Clear pathway to the parameters that drive aggregation
Clear documentation
Ongoing confirmation
Source: Health Data Consulting 18

19. Aggregating Data Defining the intent
All forms of aggregation are based on some defined intent
If the intent is to define all claims where pregnant patients have diabetes that is insulin controlled, the following concepts would need to be included in the aggregation of codes to reflect those cases:
Pregnancy
Diabetes mellitus
Insulin
Controlled
Source: Health Data Consulting Inc.

20. Aggregating Data Defining the intent
All forms of aggregation are based on some defined intent
If the intent is to cover claims to a specific dollar limit that would include patients with Down’s syndrome, a properly mapped ontology would include codes related to trisomy 21 in the set of codes since Down’s syndrome is a trisomy 21 condition.
Source: Health Data Consulting Inc.

21. Aggregating Data – Solutions Defining the intent
All forms of aggregation are based on some defined intent
If the intent is to define the cost of care, or rules or edits related to fractures of the hip. The aggregation should include in its mapping:
Subcapital fracture
Intertrochanteric fractures
Subtrochanteric fractures
Fracture of the femoral neck
Intracapsular fractures
In none of the current codes that include these concepts is the term “hip” used, but these are all hip fractures and should be included in any analysis or rules related to processing of claims with these codes if the intent is related to hip fractures.
Source: Health Data Consulting Inc.

22. Define the clinical concepts that meet the definition of the category
Aggregation Quality
Requirements
Define the clinical concepts that meet the definition of the category
What concepts should be included or excluded based on the definition?
Define the code set that:
Includes all codes that should be included
Excludes all codes that should be excluded
QA and share
Ongoing QA, and monitoring in production
Update as standards change and QA requires
Source: Health Data Consulting
Source: Health Data Consulting Inc.

23. Assure the right resources are established:
Aggregation Quality
Requirements
Assure the right resources are established:
Clinical experts
Financial experts
Coding experts
Data experts
Technical experts
Compliance experts
Source: Health Data Consulting
Source: Health Data Consulting Inc.

24. Concepts The basis of all aggregation
A concept is a thought that we have in our brain
A term is a word in an expression (one of many) that we might use to communicate concepts to others.
A code is a ‘shorthand’ set of characters that may represent one or more concepts.

25. What’s a concept? The thought vs. the expression
“Doggie”
“Steer”
“Bos primigenius”
“Cow”
“Vaca”
“Bovine”
“Beef”

26. Aggregating Data Which Taxonomy Model? Source: Health Data Consulting

27. Ontology “The conceptualization of concepts”
An ontology is a data structure that defines unique concepts and maps those concepts to a variety of expressions, including different code standards.
Concepts are represented by a “candidate term” or word, but the ontology is otherwise agnostic as to how the concept might be expressed.
Concepts might be mapped to:
An ICD-10 code
An ICD-9 code
A Snomed code
A text string
Any other expression of concepts

28. Streptococcal Pneumonia
Ontologies
Concepts related to other concepts
Ontologies allow for the ability to categorize based on a limitless number of concept relationships as expressed in metadata tags.
Source: Health Data Consulting
Streptococcal Pneumonia
Relationship
Ontological Concept
Is a type of
Pneumonia
Infection
Is a condition of
Pulmonary system
Lung
Is caused by
Streptococcus
Is a
Communicable Disease
Source: Health Data Consulting 28

29. Implied Concepts Fracture Radius Distal Extra-articular
Many expressions imply concepts that may not be explicitly stated
For example, the expression “Colles Fracture” implies the following concepts:
Fracture
Radius
Distal
Extra-articular
Volar angulation
Bone
Musculoskeletal System
Injury
Transverse

30. Aliases and Overlapping Concepts
Many common medical expressions may include the same concepts.
For example the following common medical expressions include the concept of “Hypertension”:
Benign Hypertension
Essential Hypertension
Familial Hypertension
Malignant Hypertension
Renal Hypertension
Pulmonary Hypertension
Portal Hypertension
Primary Hypertension
Secondary Hypertension
Borderline Hypertension
High Blood Pressure
Elevated Blood Pressure
HPB
Htn
Preeclampsia
Toxemia of Pregnancy
Hypertensive Crisis
180/100
Chronic Hypertension
Acute Hypertension
Source: Health Data Consulting

31. Aggregating Data Taxonomies – Which Category? Secondary Diabetes
Source: Health Data Consulting
Source: Health Data Consulting
Secondary Diabetes
ICD-9 Codes
Description
24901
Secondary diabetes mellitus without mention of complication, uncontrolled
24910
Secondary diabetes mellitus with ketoacidosis, not stated as uncontrolled, or unspecified …
Total of 20 ICD-9 codes in this category
ICD-10 Codes
E08610
Diabetes mellitus due to underlying condition with diabetic neuropathic arthropathy
E08621
Diabetes mellitus due to underlying condition with foot ulcer
E0900
Drug or chemical induced diabetes mellitus with hyperosmolarity without nonketotic hyperglycemic-hyperosmolar coma (NKHHC)
Total of 183 ICD-10 codes in this category

32. Finding concepts vs words
Postpartum / Post-partum [1,260 codes]
Source: Health Data Consulting

33. Finding concepts vs words
Renal Failure / Kidney Failure [20 codes]
Source: Health Data Consulting
Source: Health Data Consulting Inc.

34. Finding concepts vs words
Hip Fracture / Proximal Femur Fracture / Fracture upper end of the femur [1,260 codes]
* 38 codes returned if “fracture” and “hip” are used in the query
Source: Health Data Consulting
Source: Health Data Consulting Inc.

35. Finding concepts vs words
Drug induced [3,104 codes]
Source: Health Data Consulting
Source: Health Data Consulting Inc.

36. Implementation Standards to implement best practices
7/21/2018
Implementation Standards to implement best practices
Policy Template
Defining concepts to be included and excluded
Purpose of the slide:
xxx
Talking Points:
Xxx

37. Summary
Source: Health Data Consulting
Healthcare policy and payment models are becoming more focused on the nature of the patients health condition and less on the specific services delivered.
Data aggregation provides a means to categorize patients based on the nature of their health state to account for variations in risk severity and complexity.
Clinical knowledge is necessary to accurately define the parameters within data that will drive aggregation to assure a reasonable level of homogeneity of analysis.
There are specific requirements to assure that accurate definition and precisely applied logic leads to the intended data aggregation that can drive wise decisions.
Source: Health Data Consulting

38. White Papers WWW.HealthDataConsulting
Source: Health Data Consulting
Concept Based Data Aggregation
Data quality: Strategies for improving healthcare data
The Value of Quality Healthcare Data
Data Value: Breaking Old Habits
Source: Health Data Consulting
“It is possible to store the mind with a million facts and still be entirely uneducated.”   - Alec Bourne

39. Q&A
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