Health Information Analytics: Data Governance, Data Quality and Data Standards

Health Information Analytics

Data Governance, Data Quality and Data Standards

HCAD 6635 Health Information Analytics Copyright © 2016 Frank F. Wang 1

Materials Covered

• Textbook Chapter 5• Supplemental materials I uploaded to Blackboard• HIMSS Data Collections

Need to review them throughout the rest of the class

• Reference Book It all starts with a data warehouse


What Data Governance Is

• Data governance (Information Governance) is:

• The scope of data governance includes data stewardship, storage, and technical roles and responsibilities.

• It also requires leadership and processes to get the most out of an investment in analytics.

The specification of decision rights and an accountability framework to ensure appropriate behavior in the valuation, creation, storage, use, archiving, and deletion of information. It includes the processes, roles and policies, standards, and metrics that ensure the effective and efficient use of information in enabling an organization to achieve its goals.”


4

Enterprise Business Intelligence and Analytics Architecture

SatisfySource Store

Organization, Stewardship and GovernanceData and Metadata Management

Data FlowsExistingPlannedManualData Back to Sources

P_Central

FinancialSystems

(e.g. Epicor)

InfoPath

ReferralComplex

Paybase

Kwiktag

NuView(HR)

ARCHPRN

CRM

CFMS

Hosted

CATS

EBS(Payroll)

Health-eSystems(Rx) CS Stars

(Paid Claims)

Systems

Existing

Planned

External

Heat

Stage

Master DataData

Stewards

Match/Merge

Master Data Management

Scorecards & Dashboards

OLAP

EnterpriseReports

Ad hocReports

DW/ODS

Benchmark

Tasking

ClientPortal

EnterpriseBI Tool(SSRS)

SAS

Futu

re S

tate

Rec

omm

enda

tions

HCAD 6635 Health Information Analytics Copyright © 2016 Frank F. Wang

• Data Governance and Data Stewardship throughout the life cycle of enterprise information management (EIM)

Governance of Healthcare Data

• Data governance describes the concept of managing and influencing the collection and utilization of data in an organization.

• Demand for data governance growing due to increased data demand for ACO and population health management

• Tendency to operate in extremes, either too much or too little governance


Keys to Analytic Success – A Combination of Right “Sets”

• Setting the tone of “data driven” for the culture so that the organization is embracing it.

• Actively building and recruiting for data literacy among employees and provide training to physicians and other frontline staff members

• Choosing the right kind of tools to support analytics and data governance

Mindset

Skillset

Toolset


Healthcare Analytic Adoption Model

In the Healthcare Analytic Adoption Model, a robust data governance function is required in order to achieve the conditions of Level 5 maturity.

Level 8

Level 7

Level 6

Level 5

Level 4

Level 3

Level 2

Level 1

Level 0

Precision Medicine, Big Data& Prescriptive AnalyticsClinical Risk Intervention& Predictive AnalyticsPopulation Health Management& Active AnalyticsData-driven Improvement of Clinical Process & Outcome

Automated External Reporting

Automated Internal Reporting

Standardized Controlled Vocabulary& Patient Registries

Enterprise Data Warehouse

Fragmented Point Solutions

• Tailoring patient care based on population outcomes and genomics data. Treatment and engagement include IoT.

• Organizational processes for intervention are supported with predictive risk models. Fee-for-quality includes fixed per capita payment.

• Tailoring patient care based on population metrics. Fee-for-quality includes bundled per case payment.

• Reducing variability in care processes. Focusing on internal optimization and waste reduction.

• Efficient, consistent production of reports & adaptability to changing requirements.

• Efficient, consistent production of reports & dashboards widely available in the organization.

• Relating and organizing the core data content.

• Collecting and integrating the core data content.

• Inefficient, inconsistent versions of the truth. Cumbersome internal and external reporting.


Who Is On The Data Governance Committee?

Representing the analytics customers

The data technologist

The clinical data owners

The financial and supply chain data owner

Representing the researchers’ data needs

Chief Analytics Officer

CIO

CMO & CNO

CFO

CRO


The Data Governance Layers

Happy Data Analyst and

User


Different Roles Have Different Analytic Needs

Executive & Board Leadership, Sponsors

• Objectives: We need a longitudinal analytic view across the ACO of a patient’s treatment and costs, as well as all similar patients in the population we serve.

Data Governance Committee• We need an enterprise data warehouse that contains all of the

clinical data and financial data in the ACO, as well as a master patient identifier.

• We need a data analysis team experienced in descriptive and predictive analysis, as well as the IT staff who can support them.

• The following roles in the organization should have the following types of access to the EDW and our analytics system.



Data Stewards• I’m responsible for patient

registration and related data integrity.

• I’m responsible for clinical documentation in EMR and can participate in data quality improvement efforts.

• I am responsible for revenue cycle and cost accounting and can help reconcile administrative data.

Data Architects & Programmers• We will extract and organize the data

from the registration, EMR, revenue cycle, and cost accounting and load them into the data warehouse.

• “Data stewards, can we sit down with you and talk about the data content in your areas?”

• “DBAs and Sys Admins, here are the roles and access control procedures.”



DBAs & System Administrators

• Here is the access control list and procedures for approving access to this data. Let’s build the data base roles and audit trails to support these.”

Data access & control system

• When this person logs in, they have the following rights to create, read, update, and delete this data in the system.”

Data Analysts

• I’ll log into the system and build a query against the data that should answer these types of questions.

• “Data Stewards, can I cross check my results with you to make sure I’m pulling the data correctly?”

• “Data architects, I’ll let you know if I have any trouble with the way the data is organized or modeled.”


Data Governance

• Govern to the least extent required for the common good

Base your committee charter on…

Encouraging more, not less, data access

Increasing data contentin the datawarehouse

Campaigning fordata literacy

Resolving analytic priorities

Enhancing data quality Establishing standards for Master Reference Data


Data QualityEssentials of

DATAGOVERNANCE

1

Data Quality = Completeness x Validity x Timeliness of Data.

• Data quality is the single most important function of data governance.

• Low data quality negatively impacts decision accuracy or timeliness

• Related to Master Data, Metadata, Data Standards, etc.


“Quality is Free. . . . What costs money are the bad quality things — all the actions that involve not doing jobs right the first time”

Philip Crosby,Quality is Free. New York: McGraw-Hill1979


Definition of Data Quality

• Data Quality is the theory of controls ofQuality (data profiling - data cleansing)Quantity (data auditing)

• on Data for Verifying Improving

Information Accuracy.


Causes of Low Data Quality

• Type of DataMany types of data become quickly obsolete (approximately in a

month 2% of a healthcare database records change: changes of insurance, changes of residence, died)

• TechnologicalOld legacy systems and/or with few controls in data entryErrors in conversion’s routinesData coming from external sources (for example, Web)Redundant Data Architectures

• Organization’s culture and process Insufficient perception of data quality levelAbsence of commitment in improving data qualityKnowledge workers often are producers of data custom


From January through March 2007, Gartner conducted a survey among attendees of 3 BI conference in Chicago, Sydney and London. Those surveyed were made aware that their answers would be treated as anonymous. The survey lasted about 15 minutes and had 301 respondents, of which 142 were in London, 136 in Chicago and 23 were in Sydney.

Gartner’s Survey


• Data stored in separate silo systems

Are Financial Data in Healthcare Accurate and Valid?

• Large quantities of data to provide billing and patient care Estimates of 100 MB of data generated

PPPY

• Healthcare data is highly volatile Business definitions are very complex

and data metrics are constantly changing.

Different clinicians may use different definitions for the same metric and decisions can be skewed if users don’t know which metric was reported.


Functional Components

Data Quality

Data Profiling

Data Cleansing

Data Audit


Data Profiling

• Data profiling determines the level of satisfaction and accuracy maintaining the rules of the quality of data:Evaluate the degree of compliance of each data source with the

expectation of businessProvide statistical information on the distribution of values and

patterns related with each attribute (p.e. range analysis, missing value, recognition of abstract types,analysis of overloading attributes, …)

Analyze relationships and dependencies between attributes to discover hidden identifiers, embedded structures, duplicated values

Define business rules not represented in data and relation and allow to define new one

Data Profiling is responsible for identifying suitable Data Source for "Master" business entity that ensure the required quality


Data Cleansing

• Data Cleansing is responsible for standardization, reconciliation and correction of information.

• Supplies instruments for quality rules design• Implements standard processes of data quality• Normalizes data that do not respect quality rules

Data Cleansing solves missing data, corrects conflicts manages constraints, resolves relations and hierarchies to ensure the quality of data


Data Auditing

• Data Auditing is the component responsible for the logging and auditing of data memorization and migration:

• Provides services used by run-time components to collect information on data on transit

• Collects and organizes data usable by data profiling• Logs of data on transit are provided in a standard format and are collected

at enterprise level

Data Auditing is responsible for tracing the data movement in the framework information life cycle

Copyright © 2016 Frank F. WangHCAD 6635 Health Information Analytics 23

Master Data ManagementEssentials of

DATAGOVERNANCE

2 The Data Governance Committee defines, encourage use, and resolves conflicts in master data management.

• Master Data is critical business data shared among multiple systems.

• In healthcare, Master Data are devided into three types:

Core measures and algorithms—such as readmission criteria, or attributing patients to providers in accountable care arrangements

Reference data—which includes common linkable controlled vocabulary like ICD, CPT, DRG, SNOMED, LOINC, RxNorm, and order sets

Identity data—such as patient, provider, and location data standards identifiers (facility codes, department codes, etc)


What is Master Data Management (MDM)

• Master data management is, the process of linking identity data and reference data across multiple IT systems into a single, consistent point of reference.

• A more formal definition:MDM comprises the processes, governance, policies, standards, and tools that consistently define and manage the critical data of an organization to provide a single point of reference.

Mergers and Acquisitions (M&A): Because data configuration of multiple providers are usually so different, MDM is needed to merge the data.

Health information exchanges (HIEs): To successfully exchange information across locations and organizations, HIEs have to be able to reconcile master data.

ACOs: To understand and manage their patient populations, ACOs bring together health system data and payer data. This process demands a solid MDM.


Three Approaches to MDM

IT system consolidation

• To abandon best-of-breed solutions in favor of monolithic EMR (Epic, Cerner) and ERP (Lawsons and Peoplesofts) solutions.

• Large hospital systems favor this approach

Upstream MDM implementationI

• keep their disparate IT systems but map their master data through a third-party tool such as an enterprise master patient index (EMPI).

Pros: • Relatively comprehensively• MDM is handled at the level

of transactional systems, master data is reconciled at the time of the transaction.

Cons: • Complicated and expense• May be a need for more

MDM between data sources.

I

Pros: • Master data problems aren’t reconciled in

the source, they are reconciled very near the source. In addition, these systems allow for extensive manual adjudication.

Cons: • Complicated, large, expensive, and slow-

moving IT projects. • Tends to have a high failure rate.


Three Approaches to MDM

Pros: • Is a very achievable solution to the

problem.

Downstream master data reconciliation in an enterprise data warehouse (EDW)

Cons: • Drawback of this approach, is that the

mastered data is only available for analytics.

• An EDW will not solve master data

challenges at the level of transactional systems.

• When an organization needs to do analytics, but doesn’t have another MDM solution in place.

• When an organization inevitably starts integrating data sources from outside its consolidated infrastructure or EMPI.

• ACO and PHM require insurance claims data. MDM of claims data simply not available in existing healthcare providers’ solutions.

Is EDW the Right Solutions to Address MDM?


28

Enterprise BI /Analytics Architecture – Future State

SatisfySource Store

Organization, Stewardship and GovernanceData and Metadata Management

Data FlowsExistingPlannedManualData Back to Sources

P_Central

FinancialSystems

(e.g. Epicor)

InfoPath

ReferralComplex

Paybase

Kwiktag

NuView(HR)

ARCHPRN

CRM

CFMS

Hosted

CATS

EBS(Payroll)

Health-eSystems(Rx) CS Stars

(Paid Claims)

Systems

Existing

Planned

External

Heat

Stage

Master DataData

Stewards

Match/Merge

Master Data Management

Scorecards & Dashboards

OLAP

EnterpriseReports

Ad hocReports

DW/ODS

Benchmark

Tasking

ClientPortal

EnterpriseBI Tool(SSRS)

SAS

Futu

re S

tate

Rec

omm

enda

tions

HCAD 6635 Health Information Analytics Copyright © 2016 Frank F. Wang

“Metadata is akin to an encyclopedia for the data warehouse.”

Ralph Kimball, Margy RossThe Data Warehouse Toolkit – Second Edition, The Complete Guide to Dimensional Modeling John Wiley & Sons, Inc2002


Metadata Definitions and Why We Need to Manage Metadata • Metadata is information regarding the characteristics of any artifact, such

as its name, location, perceived importance, quality or value to the enterprise, and its relationships to other artifacts that an enterprise has deemed worth managing.

• Metadata is all of information in the data warehouse enviroment that is not the actual data itself.

“Metadata is Data of Data.”• Understanding metadata means knowing clearly the meaning of what is

described (Knowledge Information)• They grant a clear communication because they allow the sharing of the

same concepts with the goal to join the same target (Data Dictionary)• They represent a guide for browsing in the different areas of the Company

(Cross Knowledge)


Why Metadata Matters to Analytics/Business Ingelligence Initiatives

Traditional business intelligence has focused on collating metadata from two or more data repositories, rather than reconciling the enormous amount of assumed and undocumented metadata regarding the business process that populate those repositoriesA modern, enterprise-capable initiative was always the goal of analytics, but this has been thwarted by a belief that metadata is useful for documentation only

The creation and capture of metadata is the real key to ensuring the longevity of an information management life cycle — and analytics/business intelligence is one beneficiary, or victim, of its own behavior regarding metadata.


Types of Metadata

• Based on their content Business Metadata Technical Metadata Operational Metadata

• Based on their organization Structured (records, tables, schemas, ETL mapping, …) Unstructured (file, email, documents, diagrams, …)

• Business Metadata Common Data Model (Business Units, Business Entities, business attributes,

relations between business entities) Business Rules and Policies Business Views Ontologies (categories and terms, synonym and correlation)


Healthcare Specific Business Metadata: Revenue Cycle Management

• The provider submits a claim Claim must include at least one diagnosis code, and one procedure code for each

service rendered• Diagnosis code = ICD-10-CM (ICD-9-CM prior to Oct 1 2015)• Procedure code = CPT code or DRG code

Appointment Registration

•Registration: Demographic and insurance info

Service Rendered

• Services (Diagnosis, Procedure, Treatment, Lab)

Charge capture coding Claims submitted

Reimbursement received

Settlement with patients

Simplified Revenue Cycle Process

Charge capture process: collecting a list of all services, procedures, and supplies provided during an encounter Charge Description Master List (price list)

Coding and Code Sets are Metadata• Coding: process of translating the written diagnosis and procedures relating to a patient

encounter into a numeric classification or code• Code set: group of numeric or alphanumeric codes used to encode descriptive data

elements Tables of terms, medical concepts, medical diagnostic codes, or medical procedure

codes A code set includes the codes and the descriptors of the codes


SNOMED CT and ICDAllergic asthma 389145006

Aspirin-induced asthma 407674008

Acute asthma 304527002

Drug-induced asthma 93432008

Work aggravated asthma 416601004

Allergic bronchitis 405720007

Chemical-induced asthma 92807009

Brittle asthma 225057002

Sulfite-induced asthma 233688007

Millers' asthma 11641008

Asthma attack 266364000

Asthma night-time symptoms 95022009

Etc.

SNOMED CT

Asthma 95967001

Asthma, Unspecified, uncomplicated

J45.909ICD-10-CM

Other asthmaJ45.998

ICD-10-CM

Asthma, Unspecified Type, unspecified

493.90ICD-9-CM

OR


Cross Links between Different Code Sets

Data Quality Control: Medical Coding and Controlled Vocabulary

Hemolytic uremic syndrome (disorder) 111407006

(SNOMED CT)

Stool culture + e.coli 0157

(Lab Code)

Hemolytic-uremic Syndrome

D59.3

(ICD-10CM)

(SNOMED CT)

Hemolytic anemia (disorder) 61261009

Serum creatinine raised (finding) 166717003

Hemorrhagic diarrhea (disorder) 95545007

Abdominal pain (finding) 21522001

PatientSigns & Symptoms

Diagnosis

Lab ResultNotifiable Disease

Billing


HIPAA Code Sets

• Health Care Common Procedure Coding System (HCPCS) & Current Procedural Terminology (CPT) by American Medical Association (AMA)

• ICD-10-CM (diagnosis codes)

• ICD-10-PCS (procedures codes) by National Center for Health Statistics & CMS respectively

• National Drug Codes (NDC) by Food and Drug Administration and drug manufacturers

• Code on Dental Procedures and Nomenclature (CDT) by American dental Association (ADA)


Code Sets Used by Healthcare Providers

• Diagnosis Upper respiratory infection = J01.99 (ICD-10-CM)

• Service, procedure or test New patient, office visit, level II = 99202 (CPT) Biopsy of skin, subcutaneous tissue and/or mucous membrane(including

simple closure), unless otherwise listed; single lesion = 11100 (CPT) Immune globulin 10 mg = J1564 (HCPCS Level II)

• Physician - Inpatient and outpatient Diagnosis – ICD-10-CM Procedure – CPT

• Hospital Facility – inpatient Diagnosis – ICD-10-CM Procedure – ICD-10-PCS

• Hospital Facility – outpatient Diagnosis – ICD-10-CM Procedure – HCPCS (CPT Level I and HCPCS Level II)


Messaging

Vocabularies

Data Models

Standards for Clinical Research and Pharmaceutical

Product DevelopmentStandards for Healthcare

HL7 RPS, Clinical Genomics )E2 B ( for safety reports )

DICOM (for images )

HL7 v2.x and v 3.0NCPDP ( for Rx)

DICOMIEEE (Bedside Instruments , MIB )X12 N (for Financial data / HIPAA )

MedDRA (for drug safety )WHODrug (for drug safety )VA/KP/SNOMED (for SPL)

FDA DRLS , FDA SRS (for SPL)NCI Thesaurus ( for SPL)

LOINC (for SPL )NDF-RT (for SPL )

CDISC /RCRIM terminology (for CRF )HUGN (genomic data )

SNOMED CT ( for clinical data )ICD9CM (for billing diagnoses )

CPT (for billing procedures )LOINC (for lab )

NDF -RT , RxNorm for drugsHCPCS/APC’s ( add’l claims data )

HUGN (genomic data )

CDISCSDTM ODM LAB Define .XMLPROTOCOL (SCTP) ADaM

HL7 RIM HL 7 CDATemplatesOrder sets

Medical Metadata: Code Sets, Standards & Controlled Vocabularies


Data Model …

“Design process which aims to identify and organize the required data logically and physically”

• Which (attributes) information should be included in the database

• How the information will be used

• How the data in the database are related to each other


Data Modeling: a Conceptual Data Model of Dietician


Data Modeling: a Logical Data Model of Diabetics Diagnosis and Treatment


Design of Data Model is based on Analytic Requirement

Diabetes Patient

Typical Analyses

• How many diabetes patients do I have?

• When was there last HA1C, LDL, Foot Exam, Eye Exam?

• What was the value for each instance for the last 2 years?

• What are all the medications they are on?

• How long have they been taking each medication?

• What was done at each of their visits for the last 2 years?

• Which doctors have seen these patients and why?

• List of all admissions and reason for admission?

• What co-morbid conditions do these patient have?

• Which interventions (diet, exercise, medications) are having the biggest impact on LDL, HA1C scores?

Procedure History

Vital Signs History

Current Lab Result

Lab Result History

Office Visit

Exam Type

Exam History

Diagnosis History

Diagnosis Code

Procedure Code

Lab Type


Sample Data Model of Diabetics Diagnosis and Treatment

Data AccessEssentials of

DATAGOVERNANCE

3

Increasing access to data, including external stakeholders, community members and especially patients, is a critical Committee function.

The Committee bridges stakeholders to streamline decision making and departmental reconciliation.


Data LiteracyEssentials of

DATAGOVERNANCE

4

Data literacy can be increased by: • Education – good data from bad data• Data analysis tools• Data driven process improvement • Applying statistical techniques to improve

decision making process• Deliberate collection and dissemination of

metadata

• Data serves no purpose if intended beneficiaries cannot interpret or use the data.


Data ContentEssentials of

DATAGOVERNANCE

5

• Activity-based-costing data, genetic and familial data, bedside devices data, patient reported observations and outcomes data, and ultimately sensor data (Internet of Things) in the analytic journey.

• Recognize this is an evolutionary process and can take as many as ten or more years to complete.

• The Data Governance Committee should plan a multi-year strategy for data provisioning and acquisition


Analytic PrioritizationEssentials of

DATAGOVERNANCE

6 • The Data Governance Committee should play a major role in developing and implementing the strategic analytic plan for the C-level suite

• Analytic resource allocation should use 60/40 mix to balance top-down corporate priorities with bottom-up requests from clinical and business units.

Top-down: 60%Bottom-up: 40%


Effective Healthcare Data Governance Is Adaptive

Data Governance Life Cycle1. The Early Stage of Healthcare Data

Governance

2. The Mid-term Stage of Healthcare Data Governance

3. The Steady State of Healthcare Data Governance

• No single template for data governance.

• Experience in other industry suggests data governance approached as a rigid, idealized plan often ends up being scrapped.


The Early Stage of Healthcare Data Governance

Committee Makeup

• In the early stages, sponsors of the health system’s data governance initiative must have some decision-making authority.

THREE PHASES

DATA GOVERNANCE

1

• Leaders have a passion for using data.

• Executives, directors in the quality department, nurses, or physicians.

• Where should the initiative begin?

• Where should resources focus?

• First role will be to keep the peace, and ensure everyone impacted understands the priority.

• They must protect the integrity of the initiative to drive real quality and cost improvement.


Committee Fous

The Mid-Term Stage of Healthcare Data Governance

Committee Makeup

As your implementation of analytics continues, the nature of the decisions made by the data governance committee will change.

THREE PHASES

DATA GOVERNANCE

2

• Some members will lose interest and too busy with other initiatives.

• Viewed as an operational problem.

• Their replacements may bring new energy to the initiative.

• Responsible for monitoring the progress of existing initiatives.

How are things going?

Who is using the system?

What additional training or tools are needed to increase utilization?

What should we keep doing/stop doing/do more of?

• Start a new initiative in another department.


Committee Focus

The Steady State of Healthcare Data Governance

Council Makeup• May change the name to

governance council.

• Few of the initial leaders will still be involved. New leaders with consensus building style are likely to succeed.

• Hear customers’ concerns, make their voices heard, champion for improvement, and keep stakeholders in the loop until their concerns are addressed.

THREE PHASES

DATA GOVERNANCE

3

• Stay the course in spite of vocal resistance from other departments.

• If the initiative is working for most of the organization, actively engage the disenfranchised and bring them around.

• Continue to be responsible for monitoring the success of the initiative and prioritizing efforts.


Council Focus

Not Enough Data Governance

• Completely decentralized, uncoordinated data analysis resources-- human and technology

• Inconsistent analytic results from different sources, attempting to answer the same question

• Poor data quality, e.g., duplicate patient records rate is > 10% in the master patient index

• When data quality problems are surfaced, there is no formal body nor process for fixing those problems

• Inability to respond to new analytic use cases and requirements… like accountable care


Too Much Data Governance

• Unhappy data analysts… and their customers Everything takes too long

Loading new data

Changes data models to support new analytic use cases

Getting access to data

Resolving data quality problems

Developing new reports and analyses


How to Know If Healthcare Data Governance is Working

• Healthcare data governance is most effective when it is allowed flexibility to adapt, change and evolve when organization becomes mature. Key Metrics to Study:

• # of users

• # of requests

• # of queries, reports and page views

• # of success stories from your users!

• # of key stakeholders who are aware of your group and what you do

If governance is successful, these metrics will trend up:

• User/customer satisfaction

• Technology/analytics team satisfaction

• Time and resources it takes to answer common analytic questions

• # of requests you get to evaluate competing analytic systems

These metrics should hopefully trend down:

These metrics should stay solid, or trend upwards in rare cases:


The Triple Aim of Data Governance

• Ensuring Data Quality Data Quality = Completeness x Validity

• Building Data Literacy in the organization Hiring and training to become a data driven company

• Maximizing Data Exploitation for the organization’s benefits Pushing the data-driven agenda for cost reduction,

quality improvement, and risk reduction


Data Governance & Data Security

• Data Governance Committee: Constantly pulling for broader data access and more data transparency

• Information Security Committee: Constantly pulling for narrower data access and more data protection

• Ideally, there is overlapping membership that helps with the balance

56HCAD 6635 Health Information Analytics Copyright © 2016 Frank F. Wang

Health Information Analytics: Data Governance, Data Quality and Data Standards

Data & Analytics

Transcript of Health Information Analytics: Data Governance, Data Quality and Data Standards