healthcare information analytics

Healthcare Information Analytics

Frank F. Wang, MBA, MSPrincipal, FFW Consulting

Adjunct Faculty, Department of Management, University of New Haven

Linkedin: http://www.linkedin.com/in/frankfangwang

Email: [email protected]

http://www.linkedin.com/in/frankfangwang






mailto:[email protected]



Session 1

Class Introduction

Class Introduction

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Let us get to know each other: • Who you are• What you are doing (occupation)• What your career aspiration is• Why you are enrolled in the program and why you

are taking this class• What you hope to accomplish in the class

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

Syllabus Review

Course Materials

• Required Textbook: Healthcare Analytics for Quality and Performance Improvement by Trevor Strome (ISBN: 978-1-118-51969-1), available for purchase at the University of Bookstore.

• Additional ebooks, papers, slides and datasets will be posted in Blackboard.

• Software required to install on your personal computers for completing individual and group assignments: Microsoft Office 365, downloadable free of charge via Microsoft; SPSS downloadable free of charge via IBM. Review instructions under Technology tab, Hardware and Software section.

• HIMSS Analytics Database• Many online resources and datasets.• Also, Visit the “Analytics Primer” section on Trevor Strome’s blog

–http://HealthcareAnalytics.info/AnalyticsPrimer/

HCAD 6635 Health Information Analytics Copyright © 2016 Frank F. Wang 6

http://healthcareanalytics.info/AnalyticsPrimer/





















Other House-keeping Items

• The Center for Learning Resources (CLR) is back and ready to help you with complimentary:

· Tutoring· Supplemental Instruction (SI)· Workshops· Software Learning Assistance

• University of New Haven Enrollment Policy• Withdraw• Incomplete

• Bookmark Highline Excel 2016 website for portions of our online classes https://people.highline.edu/mgirvin/AllClasses/218_2016/218Excel2016.htm

• Download Microsoft Power Query and Power BI from https://www.microsoft.com/en-us/download/details.aspx?id=39379 and install them on your computer


https://people.highline.edu/mgirvin/AllClasses/218_2016/218Excel2016.htm

https://people.highline.edu/mgirvin/AllClasses/218_2016/218Excel2016.htm

https://www.microsoft.com/en-us/download/details.aspx?id=39379

Current Healthcare Challenges

Current Healthcare System Challenges

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Healthcare organizations are under immense pressure to:

– Improve quality and patient safety– Ensure patient satisfaction– Adopt new technologies– Demonstrate outcomes– Remain sustainable and competitive


Current Healthcare System Challenges• US Healthcare costs have been risen.

10HCAD 6635 Health Information Analytics Copyright © 2016 Frank F. Wang


• And are expected to continue to rise.



• We spent much more than other countries.



• And yet, our quality of care does not stand out (OECD 2015).


Quality of careTop third performers. Middle third performers. Bottom third performers.

Note: Countries are listed in alphabetical order. The number in the cell indicates the position of each country among all countries for which data is available. For the indicators of avoidable hospital admissions and case-fatality rates, the top performers are countries with the lowest rates.

IndicatorAsthma and COPD hospital admission

Diabetes hospital admission

Case-fatality for Case-fatality for AMI (admission- ischemic stroke based) (admission-based)

Cervical cancer survival

Breast cancer survival

Colorectal cancer survival

Australia 29 17 1 20 11 5 3Austria 28 29 27 8 19 19 7Belgium 16 20 19 20 16 12 4Canada 18 10 11 26 12 8 13Chile 6 27 31 16 25 23 n.a.Czech Rep. 12 23 11 22 13 22 21Denmark 26 14 7 17 5 11 18Estonia 27 n.a. 28 29 8 25 22Finland 10 15 9 4 6 4 7France 7 21 17 13 n.a. n.a. n.a.[…]

United KingdomUnited States

22 5 20 19 22 21 2025 24 5 3 21 2 9

Healthcare Information Systems

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The Evolution of Hospital Information Systems 1960s

HEALTHCARE DRIVERS IT DRIVERS RESULTING HITMedicare/Medicaid • Expensive mainframes

• Expensive storageShared hospital accounting systems


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HEALTHCARE DRIVERS IT DRIVERS RESULTING HIT• Hospital-wide

communications (ADT, OC, Bed Control)

• Broadened admin systems

• Departmental systems processing

• Smaller computers• Improved terminals and

connectivity

• Expanded financial and administrative systems (PA, GA, HR, MM,OP/POB)

• Results review• Selected clinical

department automation (Lab, MR,RX)

Copyright © 2016 Frank F. WangHCAD 6635 Health Information Analytics

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HEALTHCARE DRIVERS IT DRIVERS RESULTING HIT• DRGs • Networking

• Personal computers• Cheaper storage• Independent software

applications

• Integrated financial and clinical (limited) systems

• Managed care financial and administrative systems

• Departmental imaging (limited systems)


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HEALTHCARE DRIVERS IT DRIVERS RESULTING HIT• Competition,

consolidation• Integrated hospital,

provider, and managed care offering

• Broadened distributed computers

• Cheaper hardware and storage

• Expanded clinical departmental solutions

• Increased IDN-like integration

• Emergence of integrated EMR offerings


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HEALTHCARE DRIVERS IT DRIVERS RESULTING HIT• More integration• Beginnings of outcomes-

based reimbursement

• More of everything• Mobility• Emerging cloud computers

• Emerging, broad-based clinical decision support

• Broad operational departmental systems with EMR integration

• Emerging data warehousing and analytics solutions


Patient Centered Systems

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• Clinical Systems• E-prescribing• Medication Management• Inpatient computerized provider order entry

(COPE)• Electronic Medical Record (EMR) …

• Hospital Department Information Systems• Emergency• Radiology• Ambulatory care …

• Others


Business-centered and Hospital Operation Systems

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• Admission, Discharge and Transfer• E-prescribing• Medication Management• Inpatient computerized provider order entry

(COPE)• Electronic Medical Record (EMR) …

• Enterprise Resource Management• Emergency• Radiology• Ambulatory care …

• Revenue-cycle Management


Landscape of Information System Architecture in a Typical US Healthcare Provider



US EMR Adoption Model according to HIMSS Analytics

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US EMR Adoption Model according to HIMSS Analytics


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HEALTH INFORMATION EXCHANGE (HIE)

Collaborate regionally and cross- border with other statesOffer clear guidance and flexible access to consumers, employers, payers pharmas


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Health Information Exchange

Singular• Houses data from many clinical data sources

in a secure central structure

Enabling

• Enables key functions that reduces costs (reduced repeated testing, reduced risk of adverse events) and improves coordination of care

Payer

• Some HIE use cases have focus on sending ADTs (admissions, discharges, transfers) and discharge summaries to the health plans in lieu of manual processing


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DATA: A Fortuitous Byproduct of Healthcare IT Implementation

• The most commonly implemented systems were designed to automate clinical and adminstrative transactions.

• This resulted in readily available digitized data from multiple systems.

• Early innovators articulated that improving operational performance would require health systems to merge and then analyze this data.


• Regulatory mandates

• Lack of consensus on standards

Current State of Health Data: Culture, Organization, Process, People and Technology

• Reluctance to share

• Disparate data

• Proprietary data

• Multiple owners

• Large volumes

• Different in allied health industries

• New types of data (genomic, molecular)

• Legacy data

• Privacy/security


Introduction to Healthcare Analytics

What is Analytics?

Everything is vague to a degree you do not realize till you have tried to make it precise.

Bertrand RussellHCAD 6635 Health Information Analytics Copyright © 2016 Frank F. Wang 30

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Data, Information, Knowledge, Wisdom Hierarchy

Data• Symbols, facts, measurements

Information• Data processed to be useful• Provides the “who, what, when, where”

Knowledge • Application of data and information• Provides the “how”

Wisdom • Evaluated understanding• Provides the “why”


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Turning Data into Wisdom – Healthcare Example

We have 35,000 individuals in our population with diabetes.

The patients cost us $7,000 this year, a 15% increase over last year.

The national prevalence rate for diabetes is 8.3%; ours is 12%.

Hypertension is a major co-morbidity for diabetes.

Assign patient-level risk scores using a statistical model to predict which diabetics will be hospitalized next year.

Efficiently allocate care management resources to help reduce avoidable hospitalizations for at-risk patients.

Wisdom

• actionable info

5

Knowledge

• goals• targets

4

Information

• Bench-marks

• trends

3

Secondary Data

• averages• rates

2

Primary Data

• counts• sums

1


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Data Management and Information Management

Data• Generate• Collect• Organize• Validate• Analyze• Store• Integrate

Information• Disseminate• Communicate• Present• Utilize• Transmit• Safeguard


What Is Analytics

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• Analytics is the “data, statistical, and quantitative analysis, explanatory and predictive models, and fact-based management to drive decisions and actions.”(1)

• Analytics are often applied to study data using statistical analysis in order to discover and understand historical patterns within the data with an eye to predicting and improving operational performance in the future. (2)

• Analytics refers to the skills, technologies, applications and practices for continuous iterative exploration and investigation of past performance to gain insight and drive organizational planning. (3)

1.

2.

3.

Davenport TH. Harris JG. Competing on Analytics. Harvard Business School Press. 2007.

http://en.wikipedia.org/wiki/Analytics

http://www.docstoc.com/docs/7486045/Next-Generation-Business-Analytics-Technology-Trends










































































HCAD 6035: Health Information Analytics

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• Not a programming class

• Not a data science class

• Not a management class


Gartner’s Four Types of Analytics

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• Descriptive – What is happening now based on incoming data. To mine the analytics, you typically use a real-time dashboard and/or email reports.

• Diagnostic – A look at past performance to determine what happened and why. The result of the analysis is often an analytic dashboard.

•Predictive – An analysis of likely scenarios of what might happen using simulation and modeling to identify trends and portend outcomes taken. The deliverables are usually a predictive forecast.

• Prescriptive – This type of analysis reveals what actions should be taken. This is the most valuable kind of analysis and usually results in rules and recommendations for next steps. It helps organizations to optimize clinical, financial and other outcomes.


Gartner Analytic Ascendancy Model


Gartner Analytic Model Healthcare Examples

Type of Analytics

Question Answered

General Business Example

Healthcare Example

Descriptive Analytics

What Happened?

How many cars did we sell last year?

How many patients were diagnosed with HBP last year?

Diagnostic Analytics

Why Did It Happen?

Why did we only sell x cars last year?

Why did these patients develop HBP?

Predictive Analytics

What Will Happen?

If I run x advertising programs, how many cars can we sell?

What are the chances Mr. Jones’ HBP will result in a stroke?

Prescriptive Analytics

How Can We Make it Happen?

What do we need to do to sell x number of cars?

Mr. Jones should be put on x medication to prevent his HBP from resulting in a stroke.


Health Analytics

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• Healthcare organizations require better insight into their operations and accountability for their performance.

• Healthcare organizations must allow for creative use of available data and analytic tools to foster decision making – in real time and near the point of care.

• To keep up with pace of change, analytics development needs to adopt an agile approach which values innovation and experimentation.


Health Analytics

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• Healthcare decisions becoming more evidence-based and data-driven

• Healthcare organizations are methodologies that encourage and support innovation (i.e., Lean, Six Sigma)

• Multidisciplinary teams are increasingly involved in quality and performance improvement initiatives


Objectives of Healthcare Analytics

• The fundamental objective of healthcare analytics is to help peopleto make and execute rational decisions, defined as being:

Data-driven Transparent

Verifiable Robust

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Source:Rahul Saxena and Anand Srinivasan, Business Analytics: A Practitioner’s Guide, International Series in Operations Research & Management (New York: Springer Science+Business Media, 2013), 9.


Objective: Data Driven

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• Clinical and administrative decisions must be based on the best possible evidence that is generated from extensive research and data analysis.– Much “evidence” on which decisions are based is not held

to these standards.

• Analytics in healthcare can help ensure that all decisions are made based on the best possible evidence derived from accurate and verified sources of information rather than gut instinct or because a process or procedure has always been done that way.


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Being Data Driven

• Data as a driver of organizational strategy and leadership isn’t just a recent trending topic.

• The world of finance and insurance have always been data rich and data dependent.

• What’s not easy to detect is the pragmatic insight a health-care organization needs to become data driven.


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Advantages of Being Data Driven

• Use data to support the mission, thus ensuring its continuity and supporting the culture.

• If your mission is to care for patients, you want data that is in the best interest of patients.

Being Data Driven


• Data illuminates opportunities for process improvement.

• Help to avoid duplication of effort, unnecessary costs, and repeat performances of poor prior performance.

Being Data Driven• A data-driven organization

realizes fewer disruptions of operations, such as those that can arise from organizational inertia and internal politics.

Being Data Driven

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Advantages of Being Data Driven

• If medicine were not data driven, and clinicians (people) routinely ignored the evidence showing the benefits of the treatments they prescribe, we’d be back in the days of the Health Jolting Chair and Dr. Bonker’s Egyptian Oil.


• Leaders in a data-driven organization depend on the insights of data, rather than just their own expertise and opinion, to drive the business.

• Organizations runs best when its employees know where they have been, where they are, and where they are headed.

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Who or what are we

monitoring?

What are our goals?

What are we measuring?

How will we achieve them?

Data and Technology

Organization, Culture and

Process

Regularly return to these fundamentals

The Five Questions of Health Analytics

People


Objective: Transparent

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• Information silos are still a reality in healthcare due to the belief by some that withholding information from other departments or programs best maintains autonomy and control.

• Healthcare analytics can help break down silos based on program, department, or even facility by promoting the sharing of accurate, timely, and accessible information.


Objective: Verifiable

• Consistent and verifiable decision making involves a validated decision-making model that links the proposed options from which to choose to the decision criteria and associated methodology for selecting the best available option.

• With this approach, the selected option can be tested and verified, based on the available data and decision making model, to be as good as or better than other alternatives.


Objective: Robust

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• Healthcare is a dynamic environment; decisions must often be madequickly and without perfect data on which to base them.

• Decision-making models must be robust enough to perform in non-optimal conditions.– They must accommodate biases that might be introduced as

a result of missing data, calculation errors, failure to consider all available options, and other issues.

• Robust models can benefit from a feedback loop in which improvements to the model are made based on its observed performance.


Data Analytics Essentials for Today’s Healthcare Organization Copyright © 2015 Trevor Strome

Healthcare Analytics and the Information Value Chain

Performance Objectives

Quality Goals

Improvement Approach

Data

Business Processes

Analytics

What DID Happen

What IS Happening

What Will Happen

Decisions & Actions

Outcomes Evaluation

Healthcare analytics is the system of tools, techniques, and people required to consistently and reliably generate accurate, validated, and trustworthy business and clinical insight.


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Healthcare Analytics SystemAnalytics Use Cases B

usiness Context & “Voice Of The Patient”

Privacy and Security Policies

Data Governance & Stewardship

Improvement & Management Methodology

Quality & Performance Improvement.

Clinical Decisions Support

Research, Administration,& Planning

Risk Assessment & Fraud Detection

Population Health Management

Meaningful Use

Analytic Tools

Query Dashboards Reports / BI

Alerts Predictive Geospatial

Simulation Visualization

Security

Access Audit

Data Stores

Data Warehouse Data Mart RDBMS

Cloud

Integration

ETL Virtualization BPM

Data Cleansing Data Profiling

Data Sources

EMR Labs Radiology

Finance HR Claims

Social Media Devices Genomics


Healthcare Analytics Architecture

Sources of Data

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• Human-generated data

• Web and social media data

• Machine-to-machine data

• Transaction data

• Biometric data


Data Integration

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• Data integration is the combination of technical and business processes used to combine data from disparate sources into meaningful and valuable information1.– A complete data integration solution encompasses discovery,

cleansing, monitoring, transforming and delivery of data from a variety of sources.

• Data integration (DI) is a family of techniques and best practices thatrepurpose data by transforming it as it’s moved2.– ETL (extract, transform, and load) is the most common form

of DI found in data warehousing.

1.

2.

www.ibm.com/software/data/integration/

https://tdwi.org/articles/2011/05/18/data-integration-and-data-warehousing-defined.aspx


http://www.ibm.com/software/data/integration/






































































Data Stores

Source: https://tdwi.org/articles/2011/05/18/data-integration-and-data-warehousing-defined.aspx

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• At the highest level, designing a data warehouse involves creating,manipulating, and mapping models.– These models are conceptual, logical, and physical (data)

representations of the business and end-user information needs.

• Creating a data warehouse requires designers to map data between source and target models, capturing the details of the transformation in a metadata repository.

















































Data Storage and Management Models

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• Transactional Database– The database used directly by applications for data storage

and optimized for application use– Typically not analysis-friendly and provide limited analytic

capability• Operational Data Store

– Brings multiple sources of data together to enable more efficient reporting and analysis

• Data Warehouse– A repository of an organization’s electronically stored

data designed to facilitate analysis and reporting.(1)

• Data Mart– A collection of subject areas organized for decision

support based on the needs of a given business unit. (1)

Inmon, B. “Data Mart Does Not Equal Data Warehouse”. DM Direct, Nov 1999.1.


Security, Privacy, and Access

Source:http://s3.amazonaws.com/rdcms-himss/files/production/public/HIMSSorg/Content/files/CPRIToolkit/version6/v7/D03_Security_Primer(3).pdf

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• Information security is achieved by implementing policies andprocedures as well as physical and technical measures that deliver:– Confidentiality: the protection of information from

unauthorized access or disclosure.– Integrity: the protection of information from unauthorized

change(deliberate or accidental).

– Availability: the use of information as intended by ensuring that the information and other required resources are accessible for use whenever needed including during emergencies and disasters.

• Access to data is based on the individual’s role and is provided on aneed-to-know basis.


http://s3.amazonaws.com/rdcms-himss/files/production/public/HIMSSorg/Content/files/CPRIToolkit/version6/v7/D03_Security_Primer(3).pdf

















































































Common Analytical Tools

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Analytical Application Description

Business Intelligence • Provides a healthcare organization with reports and graphs used by management, decision-makers, and QI teams to analyze and understand to analyze clinical, business, and administrative trends and issues.

Statistical • Used for deeper statistical analysis not available in “standard” business intelligence or reporting packages.

Visualization • Used for developing interactive, dynamic data visualizations that aid with analysis

Data Profiling • Specific data analysis tools that help to understand and improve the quality of an HCO’s data.

Data Mining • Enables the analysis of large data sets to uncover unknown or unsuspected relationships.

Text Mining • Analysis of unstructured, text-based data to extract high-quality information.

Online Analytical Processing • Allows analysts to interactively explore data by drilling-down, rolling up, or “slicing and dicing” data.


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ACADEMIC

STATE

SOURCEDATA CONTENT

SOURCE SYSTEMANALYTICS

CUSTOMIZED DATA MARTS

DATAANALYSIS

OTHERS

HR

FINANCIAL

CLINICAL

SUPPLIES

INTE

RN

AL

EX

TER

NA

L

ACADEMIC

STATE

OTHERS

HR

FINANCIAL

CLINICAL

SUPPLIES

RESEASRCH REGISTRIES

QlikView

Microsoft Access/ODBC

Web applications

Excel

SAS, SPSS

Et al

OPERATIONAL EVENTS

CLINICAL EVENTS

COMPLIANCE AND PAYER MEASURES

DISEASE REGISTRIES

MATERIALS MANAGEMENT

Data Privacy and Information Governance

Enterprise Data Architecture of an Health Analytic System


Integration and TransformationCapture Consumption

Analytics in Action – Example Use Cases

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• Clinical Decision Support

• Population Health Management

• Process & Quality Improvement

• Administration & Planning

• Risk Assessment & Fraud Prevention


Clinical Decision Support (CDS)

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• CDS ranges from providing suggestions and evidence regarding the management of a single patient to helping manage an entire unit or department during a surge in patients.

• Goal of CDS is disseminating timely, actionable information and insight to clinical providers at the point of care when that information is required and is the most useful.


Clinical Decision Support (CDS)

• Provide insight into particular patients– Possible diagnosis given

ambiguous symptoms, incomplete history, or other missing data

– Likely outcomes (i.e., admission, long-stay) given past history of patient (and of similar patients)

• Provide insight into “near-future” of the ED

• Alert staff and management when undesirable conditions are likely to occur (i.e., offload delays, excessive wait times)

• Provide sufficient warning to take preventive action



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• Analytics can help coordinate care delivery across a population ofpatients to improve clinical and financial outcomes– through disease management– case management– demand management

• Analytics helps HCOs achieve these improvements by– identifying patient subpopulations– risk-stratifying the subpopulations (that is, identifying

which patients are at highest risk of poor outcomes)– using clinical decision support tools and best evidence to

manage patients’ and populations’ care in the best way possible.

– tracking of patients to determine overall compliance and outcomes.


The 8 Building Blocks of Successful Accountable HealthcareP

ay fo

r Rep

ortin

gP

ay fo

r Out

com

es

EHR/PMS/E-Prescribing 2. Automating and Integrating Fragmented Stakeholders

InformationExchange

(HIE)3. Sharing Clinical, Operations and Financial Information

Aggregation &Analytics 4. Aggregating Siloed Data and Gaining Insight

DecisionSupport 5. Transforming collected data into clinical knowledge

HealthcarePortals and

Medical Homes6. Making clinical information accessible and “team-based” care possible

OutcomesMeasurement &

Reporting7. Establishing Core Measures and Reporting Outcomes

RiskSharing 8. Enabling Population Based Management and Risk Sharing

Models

ConvergedMedical

Infrastructure1. Establishing Standardized and Optimized IT Platforms


9

Enterprise Data Architecture Of Population Health Management

Data Integration &

Transformation

Dashboards & Analytic ViewsContract Measures

PerformanceSummary

Baseline Expenditure Provider

Profile

Data A

ccess –Navigation &

Security

Reports

Capture Integration and Transformation Consumption

Extensible Data Architecture

Provider

Standard Data Models

Patient

Location

Claim

Reference

Other Master Data

Encounter

Patient Panel Analytics

Targeted Populations& Outcomes

Baseline Expenditures& Costs

Accountability Models

Financial Reconciliation


Health System

EMR

Billing

MPI

Provider Master

Coding

Payers

Members

Claims

• Data Enrichment • Data Profiling, Data Quality (DQ) management • Metadata layer, Controlled Vocabulary• Data Warehouse & Data Store• Data MartHCAD 6635 Health Information Analytics Copyright © 2016 Frank F. Wang 64

• Standard and Ad hoc Reporting• Data Discovery & Data Mining• Text Analytics• Statistical Analysis

Trevor Strome

Process and Quality Improvement

• Provide superior analysis of baseline data

• Identify bottlenecks and other causes of poor quality and performance.

• Guide selection of improvement initiatives that are most likely to have an impact and be successful

• Monitoring ongoing performance of processes and workflows– ensure that improvements are sustained in the

long term.


Administration and Planning

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• Analytics can help healthcare administrators optimize current (andpredict future) resource allocation requirements.

• Such resources include:• Staffing levels and scheduling• Bed requirements (type and number)• Service availability

– Diagnostic– Consulting– Allied Health

• Analytics can also assist in case-costing and efficient financialmanagement


Improving Clinical Workflow

• Throughput analytics monitors ED workflow and improves triage efficiencies.


Payer Risk Analysis and Fraud Prevention

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• Healthcare data analytics help reduce submissions of improper,erroneous or fraudulent claims– computer algorithms analyze large volumes of data, scanning for

patterns and other clues in the data that might indicate fraudulent activity and other irregularities.

• Once a manual, painstaking, and imprecise process, this is now an automated, immensely more efficient process, saving healthcare systems billions of dollars.– Centers for Medicare and Medicaid Services (CMS) achieved

$4 billion in recoveries


Cost Containment Findings

Provider #

Count Provider Name Specialty Total $

1 4836 Bing, Mark Family Practice $160,833

2 4342 Yahoo, Charles Psychiatry $143,490

3 2732 East End Urgent Care URGENT Family Practice $90,479

4 2602 Place, First MD General Practice $63,892

5 1724 Swat, Edward MD Anesthesiology $56,696

6 4312 Smith, Gregory E DPM Podiatry $54,597

7 3836 Man, Super G DPM Podiatry $49,796

8 1615 Riley, James R MD Plastic Surgery $37,970

9 3243 Avian, Bird DPM Podiatry $37,327

10 2513 Copper, Metal H DPM Podiatry $32,668

Identifying and eliminating un-necessary procedures

Necessity

• Is the procedure necessary

Savings

• How large is the potential saving and what is the estimated cost/benefit ratio?

Nail Debridement

• Nail debridement clinical guidelines. Only 2 of 5 are directly tied to a disease • Relief of pain• Treatment of infection (bacterial, fungal and viral)• Temporary removal of an anatomic deformity …• Exposure of subungal condition …• Prophylactic measure to prevent further problems …


Fraud and Abuse Detection

Provider #

Count Provider Name Specialty Total $

1 4836 Bing, Mark Family Practice $160,833

2 4342 Yahoo, Charles Psychiatry $143,490

3 2732 East End Urgent Care URGENT

Family Practice $90,479

4 2602 Place, First MD General Practice $63,892

5 1724 Swat, Edward MD Anesthesiology $56,696

6 4312 Smith, Gregory E DPM

Podiatry $54,597

7 3836 Man, Super G DPM Podiatry $49,796

8 1615 Riley, James R MD Plastic Surgery $37,970

9 3243 Avian, Bird DPM Podiatry $37,327

10 2513 Copper, Metal H DPM Podiatry $32,668

Provider1

• Charges significantly higher than his peers

Provider 2

• Specialized in psychiatry, and is not generally associated with nail debridement

Provider 5

• Practiced in a specialty that is not generally associated with the nail debridement procedure


Fraud and Abuse (F&A) Detection by Profiling Providers

Ranking Top 5 Codes by Quantity for Provider: GR0000000 – ABC Medical Group, Inc

6 Months of Service xx/xx-yy/yy, Paid in Months xx/xx-yy/yy

GR0000000Qty Rank and % Compared to

OB/GYN Groups6 Month Peer

Averages

Code Code DescTotal Dollars

Paid

Total Qty Adj Rank

% of Total Qty

Total #

Provs

Peer Avg Dollars

Paid

Peer Avg Qty

81025-TC Urine pregnancy test $12,560.60 2,710 #1 or 19% 65 $1,022.93 220

Z9752Family planning counseling (15 minutes) $33,086.45 1,735 #1 or 21% 55 $2,778.90 149

Z6410Perinatal education, individual, each 15 minutes $9,511.71 1,131 #9 or 3% 91 $3,657.38 435

Z6204

Follow-up antepartum nutrition assessment, treatment and/or intervention; individual, each 15 minutes $7,569.00 900 #5 or 4% 96 $2,074.14 247

Z1034 Antepartum follow-up visit $48,625.92 804 #16 or 2% 195 $11,996.08 203

Outlier detection based on profiling provide data.


healthcare information analytics

Data & Analytics