An exploratory evaluation and assessment on the value of analytics in healthcare

1. HEALTHCARE
BUSINESS INTELLIGENCE AND
ANALYTICS
AN EXPLORATORY EVALUATION AND ASSESSMENT ON
THE VALUE OF ANALYTICS IN HEALTHCARE
Nick Sullivan, MHA

2. 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

3. 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.

4. SHOPPING FOR HOLIDAY GIFTS A LOT
LIKE HEALTHCARE?
• What am I going to get for • What information could I use
my family this year? about my family to make a
better decision?
• 2 parents, 4 sisters, 4
nephews, 6 neices, 2 brother
in-laws • Age
• How much am I willing to • Gender
spend in total? • Needs
• How old are my nieces and • Wants
nephews? • Interests
• Does that affect whether or
not I get them clothes or • Current Trends
toys? • Satisfaction with previous
• Can I get them the same gifts
gifts and not feel bad about • Frequency of use of previous
it? gifts
• Did they like my gift from last
year?
Questions + (∑Facts/Beliefs) – Noise = Knowledge  Better Decisions  Better Outcomes

5. 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

6. 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?

7. 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:
Clinical Administrative Financial Operational
• But we already create departmental reports, correct?
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

8. THE HEALTHCARE ENTERPRISE INTELLIGENCE
FRAMEWORK
Source Data Staging Data Warehouse Customization Client
Ad Hoc Query
Practice
Finance EMR Mgmt.
Service
Line
Lab Pharmacy Transform
Disease
Specific
HR Payroll Clean
Condition KPI‟s
Fully Integrated
Extract Scrub
Standardized Scorecards, Reports,
Surgery Dept. Merge Load Patient Dashboards
Validate Historical
Centers Sprdshts Registries
Confirm One Version of Truth
Anomaly Detect Secure Strategic
Legacy Planning
Physician Mapping
Clinical
Clinic Service
Sys
Line
Graphs & Charts
Patient
Scheduling
Satisfaction Errors Costing,
Finance Multidimensional
Data Mining
Market Reg. & P4P Operating
Data Reqmts. Room
Metadata

9. 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 Data Warehouse (OLAP)
(OLTP)
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

10. 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.

11. 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.

12. 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

13. 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

14. OLAP CUBE
Provides users the ability to
create relationships and
multidimensional views of
different data sources 33 71
AMI 22 1
Can perform functions such as: 1 12 61 1
Disease
CHF
• slicing
• dicing COPD 54 10 15 81
• pivoting
• rolling up Pneum
42 122 132
19 11
• drilling down
A B C D
Physician

15. CLIENT: REPORTING AND ANALYSIS
Ad Hoc Query: Highest level of client
customization. Gives user liberty within certain
constraints to work directly with raw data
Level of Data Granularity
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

16. ANALYTICS AND HEALTH REFORM
Patient Protection and Affordability of Care Act
- Signed into law 2010
Focuses on Triple Aim of: Increased Access, Improved Quality,
Cost Reduction
OLD: NEW:
Fee for Service
Access Quality Cost Value Based Care
Emphasizes Value-Driven Care and shift from fee-for-service

17. ANALYTICS AND HEALTHCARE
• Healthcare analytics is intended to improve
decision making. Healthcare Decisions can be
broken into: Tactical, Operational, Strategic
Purpose and Goal Types of Measures
Analytical Uses
Patient Satisfaction Disease Mgmt. Protocol Adherence
Order Set Compliance Episode Profiling
Patient Level
Tactical Decisions Medication Errors Risk Scoring
Provider Performance Activity Based Costing
Care Process Variance Process Mapping
Care Process Supply Use Value-Add Analysis
Operational Stewardship & Cost
Process Based Costing Care Coordination
Management
Gap Identification
MD Network Analysis Staffing Predictions
Price Setting Pattern and Trend Recognition
Strategic Planning & Growth
Utilization Predictions Agile Marketing
Resource Channeling Community Needs Assessment

18. DRIVING VALUE
• As reimbursement models
change, focus will shift from
volume to value and
delivering on outcomes.
Value = Quality/Cost
Value Based
2014 Reimbursement Model: Purchasing
Healthcare providers must use
data to measure, track and
improve performance in these
areas.

19. USING DATA TO CREATE VALUE
Place analytical focus on three aspects of care:
Process, Cost and Outcomes

20. Care process and improvement feedback loop

21. 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 What works best and
• Treatment regimens produces the best
• Supplies outcomes? Let data tell
• Care channeling you, standardize and
deploy.
• Disease Management Techniques
• Variation: deviation from standardized processes
• Helps control costs and identify areas for improvement

22. 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

23. 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
ED visit most
visits on
common
average
Young
15Annual
Well-visit most
visits on
All Sickle common
average
Cell Patients Old

24. COSTS OF: REPORTING, TIME TO
ACTION & HIDDEN INSIGHT
Analytics helps reduce cost by: Productivity cost incurred to
1. Reducing reporting costs. perform activity
2. Increasing “time to action”. Time to reach decision, “action”
3. Freeing hidden insight.
Insight gained from business user
having access to analytics
Business Leader/Analyst
has goal/question in
mind
Business Decision is
Business Decision is
made
made
Contacts data owner
Owner queues request Data Analyst validates, Analyst presents to Business Leader
aggregates, integrates, Information to Business Evaluates Strategies for
models, data Leader Solving Problems

25. 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.

26. SELF-SERVICE BUSINESS INTELLIGENCE
No question is a bad question. Putting the power of analytics at the
fingertips of business experts and enabling them to question the data
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?

27. COMPETING WITH ANALYTICS
EXAMPLE
Question: Why is the Cardiovascular Service Line losing market
share to the competitor?
Data Request: please provide report that shows market share by:
With Analytics: VP Drill down capability
50 – 64Pt. Scheduling
Cohort 50% Drop in Cases
Age System shows
Fewer 50-64 age
Ethnicity patients scheduled
?
Product Increase # of nurse
and brand
awareness is low
Zip Code practitioners to
improve throughput
Practice Mgr.: MD’s
backlogged due to
Payor Group elderly throughput
Begin
Service Type billboard
campaign
65+ is directly to market
Problem is not
correlated with to seniors
awareness but
cardiovascular
ACCESS
demand

28. Disease "Hotspotting"
Readily identifies patients with specific diseases.
Alerts, Notifications •Allows for identifying patients with high cost diseases or patients with potential to worsen due to the presence
and Decision
of a combination of predefined factors. Alert triggers action to monitor patients with targeted follow-up and
intervention strategies.
Support Systems Gap Identification
Tracks whether patients received a service or not in a proces
of care.
•Removes "chance" from care regimen by hardwiring specific events into process, alerting when a gap is
Speeding up the present. Patients can be auto-populated onto a list for specific follow-up for connection to missed event.
decision making Care Episodes Monitors variance from pre-defined episodes of care.
process. •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.)
Risk Scoring Attaches risk score to each patient based on severity of illness
and presence of comorbidities.
•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.
Patient Registries Creates running database or list of patients by disease type to
facilitate population health management
•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.
Continuity of Care Identifies when patients expose the system to risk by receiving
Leakage care from provider's outside of system
•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.)
GIS Enabled Activity Creating instant "location effect" by mapping operational ,
Mapping market and competitive data
•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.

29. 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

30. 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