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Data Analytics

Content Provided by the

CSA Excellence Presentation Series

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Changing Landscape

• Many healthcare organizations are experiencing positive ROI results in data analytics and reporting technologies

• New payment models such as Accountable Care Organizations are driving the need for more meaningful data

• Industry is fast paced with many competing priorities

• Everyone wants Meaningful Data!

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Big Picture

• Patient Data lives everywhere in the healthcare environment

• The legal health record is multifaceted and ever-changing due to health information exchange and the quest for interoperability

• Organizations must do more with less and still provide quality care with focus on lowering costs

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Who Wants Your Health

Information?

HIMSS Health Information Exchange Wiki / HIEDefinition

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The Management of

Information

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AHIMA’s Definition of

Information Governance

An enterprise-wide framework

for managing information

throughout its lifecycle and

supporting the organization’s

strategy, operations,

regulatory, legal, risk, and

environmental requirements

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Other Definitions for IG

• Having control over all the information that flows through, into, out of, and stays in, an organization. MetLife

• It’s about the policies and the practices that enable you to make decisions about how you’re going to manage your information. NextPage

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IG in Healthcare Includes:

Data Quality and Data Governance

IT Governance

Legal, e-Discovery, e-Disclosure

Standards, Best Practices, Guidelines, Principles

Privacy, Security & Confidentiality

Lifecycle Mgmt: Creation & Capture, Retention, Archiving, Preservation, and Disposal

Clinical Data Capture, Coding, CDI

Compliance, Risk Management, Patient Safety

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IG for Healthcare

• IG is not JUST needed in hospitals….but in all types of

delivery settings, and across the healthcare

ecosystem….. Wherever information is exchanged,

used, administered, analyzed, released, stored,

archived or deleted/destroyed, it must be governed.

• IG is NOT an IT project… it is not a project at all… but

an ingraining of principles, a framework, rules and

processes for managing and controlling information

across the enterprise.

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Information Governance-

Healthcare

• There must be acceptance that information is a

mission-critical asset to be controlled throughout its

lifecycle.

• Data and information regardless of medium must be

included

• All information must be governed….IG in healthcare

cannot be limited to health information…Data and

information supporting the business of the entire

organization must be governed.

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What is Data Governance

(DG)?

Data Governance is:

– Defined “as a set of processes that ensure that

important data assets are formally managed

through the enterprise” (Sarsfield 2009, 23)

– Generally focused on master or critical data

and the processes that capture, process and

audit data.

Kloss, Linda. (2015) “Implementing Health Information Governance, Lessons From the Field

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Need for Good Data

• Too much data being collected and not

used.

– “Dark Data”

• Causes inefficiencies and lack of direction

• Ultimately interferes with quality of care

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Data Quality Management Model

AHIMA. "Data Quality Management Model (Updated)." Journal of AHIMA 83, no.7 (July 2012): 62-67.

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Why Collect Data?

• For safe and effective patient care

• For stable business operations

• For financial viability

• To…Transform it into usable, meaningful information to reach these goals.

– If achieved, business intelligence has been derived from our data and information assets.

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Too Busy?

“It is not enough to be busy. So are the ants. The question is: What are we busy about?” Henry David Thoreau

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Analysis Paralysis

• Too much information

• Irrelevant information

• Not knowing how, when

and where to stop collecting

data1

• No clear strategic plan

http://www.slideshare.net/Dexymine/analysis-paralysis-31078406

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Data Capture

• Internal:

– Starts with patient registration

– Electronic discrete data elements through

templates, forms, barcodes, direct entry,

speech recognition with or without NLP,

dictation and transcription

– Unstructured data such as handwritten notes

or scanned images

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Data Capture Best Practices

• Evaluate the data and determine its placement in the

record

• Collect the data in a standardized format using templates

or discrete fields to make retrieval for reporting easier

• Routinely audit a sample of records that were collected

using the data capture methods described above

• Acquire primary and secondary data from existing

internal or external data sources

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How Do We Use Data?

• Financial

• Clinical

• Operational

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Financial

• Case Mix Index

• DRG Analysis

• APC reimbursement

• Revenue Cycle

• Hospital Acquired Conditions

– Present on Admission (POA) Indicators

• CMS Value Based Purchasing

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Financial – VBP Measures

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Clinical

• Registry Data

– Cancer Registry

– Trauma Registry

– Birth Defect Registry

• Infection Rates

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Clinical - Example

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Operational

• Physician Productivity

• Daily Department Stats

• Average Length of Stay

• Hospital Readmission Rates

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Operational – Example

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Analysis Versus Analytics

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Analysis

• Process of deconstructing or breaking a complex issue, part, topic or substance into smaller parts to gain a better understanding

• Is HIM familiar with this process?

– Examples:

– Coding

– Auditing

– Workflow assessments

– Quality measurements

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Analysis

• May or may not be used with technology

• Can include information such as gaps,

sequences and measures

• But, this is not necessarily Analytics

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• Applying scientific or quantitative methods

to discern patterns and provide insights

using:

– Statistics

– Algorithms

– Data mining

– Machine learning

Analytics

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Analytics

• Data Mining and Machine Learning are closely tied and often used interchangeably

– Data Mining is the process of extracting and analyzing large volumes of data to find hidden relationships or patterns to predict behaviors

– Machine Learning studies algorithms and computer programs to enhance learning experiences and is a core concept of artificial intelligence

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Analytics

• Business Intelligence

• Big Data

• Data Science

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Analytics

• Decision Support Systems is a broad term used to describe a support tool such as Computerized Provider Order Entry (CPOE)

• Example: – HCO partners with DSS vendor to enhance

compliance for severe sepsis and septic shock.

– Searched for a correlation between compliance and patient mortality and to identify patterns from previous patients to construct a predictive model for early detection

– 100 lives were saved in the first nine months

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Analytics

http://www.tcshealthcare.com/sites/default/files/140507%20Press%20Release%20TCS%20Trend%20Report%208%20Announcement_FINAL.pdf

• Trends:

– Traditional reporting tools such as Excel,

Crystal Report, Access remain is the most

common applications

– Emerging applications like Tableau have a

smaller part of the market share

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Analytics – Trending Example

febrile neutropenia A condition marked by fever and a lower-than-normal number of neutrophils in the blood. A neutrophil is a type of white blood cell that helps fight infection. Having too few neutrophils increases the risk of infection. [Source: NCI Dictionary of Cancer Terms]

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Analytics

• Importance of dashboards and

visualization capabilities:

– Ability to manipulate reports and

data presented

– Convenient access to and delivery

of information (ex mobile devices)

– View trends for individual patients

and large sets of data

http://www.tcshealthcare.com/sites/default/files/140507%20Press%20Release%20TCS%20Trend%20Report%208%20Announcement_FINAL.pdf

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Analytics – Dashboard Example

Key features: • Target values

• Indication of trends

• Monthly and annual figures

• Drill down to unit level

• Actionable metrics

• Clinical input!

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Analytics

• Analytics often includes data mapping and

visualization techniques to further

communicate insight to bring meaning to

the data.

• Does the data mean anything without

being able to communicate patterns?

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Analytics – Visualization

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Examples of Using Data for

Meaningful Analytics

– Who is at high risk for re-admission?

– Is fraudulent activity occurring?

– How are new payment models affecting

reimbursement?

– How does our EMPI duplicate rate affect

current and future plans for our HIE efforts?

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Episode Performance

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Notes

• Data Analysis Toolkit, Updated 2014

• An Introduction to Information

Governance, CSA Excellence

Presentation Series

• Pocket Glossary of Health Information

Management and Technology, Fourth

Edition

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Questions