A N E X P L O R A T O R Y E V A L U A T I O N A N D A S S E S S M E N T O N

T H E V A L U E O F A N A L Y T I C S I N H E A L T H C A R E

HEALTHCARE BUSINESS INTELLIGENCE AND

ANALYTICS

Nick Sullivan, MHA

ABOUT ME

• UNC Grad (‘07 B.A Public Policy, ‘12 Masters Healthcare Administration)

• Gained interest in analytics while working as grad assistant at School of Public Health Business & Finance office

• Currently employed as Administrative Fellow at Novant Health in Charlotte, NC

• Looking to advance knowledge technical, clinical, strategic and financial aspects of healthcare business intelligence

• San Francisco 49er Fan

ABOUT MY EXPERIENCE

• Healthcare is a mammoth industry

• Healthcare is undergoing sweeping and disruptive

changes

• Healthcare leaders are inundated with multiple

competing and uncertain priorities

• Healthcare organizations must learn to do more

with less

• …

• Healthcare will get better.

SHOPPING FOR HOLIDAY GIFTS A LOT LIKE HEALTHCARE?

• What am I going to get for my family this year?

• 2 parents, 4 sisters, 4 nephews, 6 neices, 2 brother in-laws

• How much am I willing to spend in total?

• How old are my nieces and nephews?

• Does that affect whether or not I get them clothes or toys?

• Can I get them the same gifts and not feel bad about it?

• Did they like my gift from last year?

• What information could I use

about my family to make a

better decision?

• Age

• Gender

• Needs

• Wants

• Interests

• Current Trends

• Satisfaction with previous

gifts

• Frequency of use of previous

gifts

Questions + (∑Facts/Beliefs) – Noise = Knowledge Better Decisions Better Outcomes

RELEVANCE TO HEALTHCARE

• Grown Accustom to shaping processes and decisions based on:

• intuition,

• provider preference,

• amount and type of resources available,

• competing priorities,

• vested financial interest and

• incentives aimed at more care is better care (do it all)

• What if we had information to make decisions based on individual

patient characteristics and evidence gleaned from previous

encounters with the disease?

• How do we provide timely, efficient and cost-effective care that

resulted in ultimate patient satisfaction? ANALYTICS

THE RISING TIDE OF DATA

• World becoming awash in data, growth at 60% annually

• Widespread Healthcare EMR implementation will rapidly expand access to data

• How does healthcare make the most of its growing data?

THE VALUE-ADD OF ANALYTICS

• Healthcare Organizations must find ways to converge different types of data

to glean insight on critical aspects of running the enterprise:

• But we already create departmental reports, correct?

Clinical Administrative Financial Operational

Analytics v. Reporting

Business Intelligence Area Reporting Analytics Analyst Primary Function Building Questioning

Use of Visuals Configuring Examining

Data Relationships Consolidating Interpreting

Data Sourcing Collecting Connecting

Data End Game Summarizing Validating

Communication Method “Push” “Pull” Data Lifespan Static Dynamic

Data Orientation Look Back Look Ahead

THE HEALTHCARE ENTERPRISE INTELLIGENCE FRAMEWORK

Staging Data Warehouse Source Data Customization Client

Finance EMR

Lab Pharmacy

HR Payroll

Surgery

Centers

Dept.

Sprdshts

Legacy

Clinical

Sys

Scheduling

Physician

Clinic

Patient

Satisfaction

Market

Data Reg. & P4P

Reqmts.

Extract

Errors

Transform

Load

Clean

Condition

Scrub

Merge

Validate

Confirm

Anomaly Detect

Mapping

Fully Integrated

Standardized

Historical

One Version of Truth

Secure

Metadata

Service

Line

Disease

Specific

KPI’s

Patient

Registries

Strategic

Planning

Service

Line

Costing,

Finance

Operating

Room

Practice

Mgmt.

Multidimensional

Data Mining

Scorecards, Reports,

Dashboards

Graphs & Charts

Ad Hoc Query

KEY ENTERPRISE ELEMENTS

Source Data: data that is critical to running the business.

- Typically operational in nature and built to handle large

numbers of simple, predefined read/write transactions using

OLTP

- Integrated into data warehouse for analytical use (OLAP)

Focus Area Operational System (OLTP)

Data Warehouse (OLAP)

Orientation Application Oriented Subject Oriented

Business Use Used to run business Used to analyze and optimize business

Data Presentation Detailed & Discrete Summarized and refined

Time Orientation Current, Up to Date Snapshot of Data

Data Relationships Isolated Integrated

Frequency of Use Repetitive Access Ad-hoc access

Primary User Business Processer Business Analyst

EXTRACT, TRANSFORM, LOAD (ETL)

ETL: Process of gathering, preparing and integrating data into the

data warehouse

Extraction: data taken in “as-is” format from source

Transform: data cleaned, validated and confirmed for eligibility for

inclusion into data warehouse

Load: maps source data attributes to schema of data warehouse

Most critical part of data warehousing process as

this defines, creates and maintains the integrity

of the enterprise data.

DATA WAREHOUSE

Repository for organizational data, ultimate source for

reporting and analysis:

Subject-oriented

• The data in the data warehouse is organized so that

all the data elements relating to the same real-

world event or object are linked together.

Non-volatile

• Data in the warehouse is never deleted or

replaced. Once the data is in the data warehouse,

it is permanent and kept for reporting purposes.

Integrated

• Contains data from nearly all of the organizations

operational systems.

Time-variant

• Contains a component of time for every

operational data element.

CUSTOMIZATION

Datamarts: subsets of data warehouses that contain a much smaller set of data typically focusing on one

business area.

• quicker access to specific information that certain groups

• Dependent on data warehouse, does not interfere with integrity

• Gives “ownership” to individual business units over specific data

• Allows business units to create and track metrics, targets, KPI’s and

performance goals

CUSTOMIZATION

• Online Analytical Processing (OLAP): software

process that provides a multidimensional view of

enterprise data.

• Fast

• Consistent

• Iterative process

• Reflects familiarity with user understanding of business

• Uses data cubes to create multidimensional views

OLAP CUBE

132

AMI

CHF

COPD

Pneum

A B C D Physician

Dis

ease

22

1

54

42

61 1

33 1 71

12

15 81 10

122 11 19

Provides users the ability to

create relationships and

multidimensional views of

different data sources

Can perform functions such as:

• slicing

• dicing

• pivoting

• rolling up

• drilling down

CLIENT: REPORTING AND ANALYSIS

Ad Hoc Query: Highest level of client

customization. Gives user liberty within certain

constraints to work directly with raw data

Multidimensional Data Mining: Use of OLAP tool

and cube to create various views

Scorecards, Dashboards Reports: pre-defined

views and KPI’s for specific business units and/or

goals. May allow drill down or roll up function

Graphs & Charts: typical visual representation of

predefined metrics and views

Lev

el o

f D

ata

Gra

nu

lari

ty

Patient Protection and Affordability of Care Act

- Signed into law 2010

Focuses on Triple Aim of: Increased Access, Improved Quality,

Cost Reduction

Emphasizes Value-Driven Care and shift from fee-for-service

ANALYTICS AND HEALTH REFORM

Access Quality Cost OLD:

Fee for Service

NEW:

Value Based Care

ANALYTICS AND HEALTHCARE

• Healthcare analytics is intended to improve

decision making. Healthcare Decisions can be

broken into: Tactical, Operational, Strategic

Purpose and Analytical Uses

Goal Types of Measures

Tactical Patient Level

Decisions

Patient Satisfaction Disease Mgmt. Protocol Adherence

Order Set Compliance Episode Profiling

Medication Errors Risk Scoring

Provider Performance Activity Based Costing

Operational Care Process

Stewardship & Cost Management

Care Process Variance Process Mapping

Supply Use Value-Add Analysis

Process Based Costing Care Coordination

Gap Identification

Strategic Planning & Growth

MD Network Analysis Staffing Predictions

Price Setting Pattern and Trend Recognition

Utilization Predictions Agile Marketing

Resource Channeling Community Needs Assessment

DRIVING VALUE

• As reimbursement models

change, focus will shift from

volume to value and

delivering on outcomes.

2014 Reimbursement Model:

Healthcare providers must use

data to measure, track and

improve performance in these

areas.

Value = Quality/Cost

Value Based

Purchasing

USING DATA TO CREATE VALUE

Place analytical focus on three aspects of care:

Process, Cost and Outcomes

Care process and improvement feedback loop

STANDARDIZATION, VARIATION & WASTE

• Standardization: applying uniformity across the enterprise throughout every element of care to

increase likelihood of desired outcome.

• Use data to determine which elements to standardize

• Order sets

• Treatment regimens

• Supplies

• Care channeling

• Disease Management Techniques

• Variation: deviation from standardized processes

• Helps control costs and identify areas for improvement

What works best and

produces the best

outcomes? Let data tell

you, standardize and

deploy.

WASTE

• By standardizing care processes, and applying

analytics, variation is spotted and waste or non-

value adding elements are discovered.

• Equates to a resource that has not yet been discovered or

exploited for its value.

• Increases capacity to perform primary business functions

• Saves time by omitting non-value adding steps

• Decreases cost of providing care

DRILLING DOWN TO REDUCE COSTS

• Healthcare providers must deliver on the cost element of the

Value = Quality/Cost equation.

• Data and drill-down analytics helps remove unnecessary costs

• Processes can be analyzed at different levels:

• Organization (All diabetes patients)

• Population (Females, age 32 -45)

• Patient (Ms. Jones)

2 Annual

visits on

average

15Annual

visits on

average

ED visit most

common

Well-visit most

common All Sickle

Cell Patients

Young

Old

COSTS OF: REPORTING, TIME TO ACTION & HIDDEN INSIGHT

Analytics helps reduce cost by:

1. Reducing reporting costs.

2. Increasing “time to action”.

3. Freeing hidden insight.

Business Leader/Analyst

has goal/question in

mind

Data Analyst validates,

aggregates, integrates,

models, data

Business Leader

Evaluates Strategies for

Solving Problems

Contacts data owner

Owner queues request

Analyst presents to

Information to Business

Leader

Business Decision is

made

Productivity cost incurred to

perform activity

Time to reach decision, “action”

Business Leader/Analyst

has goal/question in

mind

Business Decision is

made

Insight gained from business user

having access to analytics

COMPETING WITH ANALYTICS

Healthcare is no longer “build and they shall come”

- resources have tightened

- patients consumers have choice

• Using data to enhance reputation and recognition

• Appeal to customers with and ability to deliver on

promises and showcase facts

• Provide patients with customized, patient centered

care using data at fingertips.

SELF-SERVICE BUSINESS INTELLIGENCE

1. Who is effectively managed? Why? (Age, Zip, Ethnicity, Payor, Gender)

2. Who’s not, why? (seasonality, facility, comorbidities, procedure, visit frequency, appropriate care relationships, age)

3. What is their average total cost, LOS , and #of tests/visit?

4. Did they acquire any infections? If so What kind?

6. Of those not managed, have they had ED Visits? How Many? Time between visits? Did they get better or worse post ED?

7. Who developed post care complications? Why? (procedure error, wrong test, wrong drug, staff competence, infection)

8. What kind of complications where they?

9. Who was readmitted to hospitals?

10. What was their reason for admission?

11. Did they have intermittent communication with provider? If so, who, what type, how many?

12. What do MD, RN Manager notes say about the patients? Any pattern amongst groups?

13. Were they all from same facility?

14. Were they all from same facility?

No question is a bad question. Putting the power of analytics at the

fingertips of business experts and enabling them to question the data

Question: Why is the Cardiovascular Service Line losing market share to the competitor?

?

Data Request: please provide report that shows market share by:

Age

Ethnicity

Zip Code

Payor Group

Service Type

50 – 64 Cohort 50% Drop in Cases

Begin

billboard

campaign

to market

to seniors

Product and brand

awareness is low

Pt. Scheduling

System shows

Fewer 50-64 age

patients scheduled

Practice Mgr.: MD’s

backlogged due to

elderly throughput

65+ is directly

correlated with

cardiovascular

demand

Problem is not

awareness but

ACCESS

Increase # of nurse

practitioners to

improve throughput

With Analytics: VP Drill down capability

COMPETING WITH ANALYTICS EXAMPLE

Readily identifies patients with specific diseases. Disease "Hotspotting"

•Allows for identifying patients with high cost diseases or patients with potential to worsen due to the presence of a combination of predefined factors. Alert triggers action to monitor patients with targeted follow-up and intervention strategies.

Tracks whether patients received a service or not in a proces of care. Gap Identification

•Removes "chance" from care regimen by hardwiring specific events into process, alerting when a gap is present. Patients can be auto-populated onto a list for specific follow-up for connection to missed event.

Monitors variance from pre-defined episodes of care. Care Episodes

•Monitors activities of predefined episodes of care to avoid overutilization of services and incurrence of unnecessary costs. Episodes are grouped by disease type (Coronary Heart Failure, Chronic Obstructive Pulmonay Disease, Heart Attack, etc.)

Attaches risk score to each patient based on severity of illness and presence of comorbidities.

Risk Scoring

•Creates opportunity for providers to adequately distribute resources to patients most in need. High risk patients may depend on type of condition, medical history, demographic facts, compliance history, transition to home status, etc. This is a predictive modeling mechanism to help providers mitigate risk.

Creates running database or list of patients by disease type to facilitate population health management

Patient Registries

•Allows providers to stratify patients to better understand the clinical dynamics of their disease and its impact on operations and finances. By grouping patients into groups such as high/low cost, high/low utilization, positive/negative outcomes, relationships between clinical activity and outcomes can be created to determine best practices as well as identify patients and processes that need attention.

Identifies when patients expose the system to risk by receiving care from provider's outside of system

Continuity of Care Leakage

•For healthcare organizations that are focused on providing care for the entire patient continuum, when patients leave the system to receive care, the organization becomes exposed to risk. When patients receive care elsewhere, providers have no control over the types of care, outcomes or costs associated with that visit. By creating alerts, providers can be proactive in ensuring that patient outcomes are not jeopardized. By aggregating alerts, providers also gain insight into why patients are leaving the system (access, capacity, lack of follow-up, dissatisfaction, etc.)

Creating instant "location effect" by mapping operational , market and competitive data

GIS Enabled Activity Mapping

•By placing operational data onto a GIS enabled map, healthcare organizations can instantly see how their activity interacts across its primary and secondary service areas. This provides the organization with insight on service area demand, capacity, performance, competitive advantage/disadvantage, demographic alignment and several other location-based.

Alerts, Notifications

and Decision

Support Systems

Speeding up the

decision making

process.

CHALLENGES

• Healthcare Organizations are Overwhelmed with IT priorities • EMR implementation, training and troubleshooting is a huge task

• Data is aplenty and very much unalike • Structured and unstructured data will make integration difficult

• Cultural Barriers will slow the buy-in and uptake process • Business units feel ownership of data, threatened by increased access

• People are naturally resistance to change • Bringing “science” to decision making will take time for people to adopt

• Reimbursement is not a certainty • Data may help with financial vitality but it is not the sole answer

FINAL THOUGHTS

• Governance will play critical role in making BI a reality

• All decisions are highly scrutinized and assessed from the

highest levels of the organization

• Data has revolutionized many industries

• Healthcare is next on the innovation curve

“in times of great change, it is the learners who inherit the future,

the learned usually find themselves equipped to live in a world

that no longer exists”

- Eric Hoffer, Reflections on the Human Condition