healthcare business intelligence
DESCRIPTION
An exploratory evaluation and assessment on the value of analytics in healthcareTRANSCRIPT
A N E X P L O R AT O RY E VA L U AT I O N A N D A SS E SS M E N T O N T H E VA L U E O F A N A LY T I C S I N
H E A LT 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?
Stressed Holid
ay Shopper
(Me)
• 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. ReportingBusiness Intelligence Area Reporting Analytics Analyst Primary Function Building QuestioningUse of Visuals Configuring ExaminingData Relationships Consolidating InterpretingData Sourcing Collecting ConnectingData End Game Summarizing ValidatingCommunication Method “Push” “Pull”Data Lifespan Static DynamicData Orientation Look Back Look Ahead
THE HEALTHCARE ENTERPRISE INTELLIGENCE FRAMEWORK
Staging Data Warehouse
Source DataCustomizatio
nClient
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
CleanConditionScrubMergeValidateConfirmAnomaly DetectMapping
Fully IntegratedStandardized
HistoricalOne Version of
TruthSecure
Metadata
Service Line
Disease Specific
KPI’s
Patient Registries
StrategicPlanning
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 OrientedBusiness Use Used to run business Used to analyze and optimize
businessData Presentation
Detailed & Discrete Summarized and refined
Time Orientation Current, Up to Date Snapshot of DataData 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 sourceTransform: data cleaned, validated and confirmed for eligibility for inclusion into data warehouseLoad: maps source data attributes to schema of data warehouse
Most critical part of data warehousing process asthis defines, creates and maintains the integrityof 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
12
3
Dis
ease
Care
Proc
ess
22
1
54
42
61 1
33 1 71
12
15 8110122 1119
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
Level of
Data
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 ServiceNEW:
Value Based Care
ANALYTICS AND HEALTHCARE
• Healthcare analytics is intended to improve decision making. Healthcare Decisions can be broken into: Tactical, Operational, StrategicPurpose and
Analytical Uses
Goal Types of Measures
Tactical Patient Level Decisions
Patient Satisfaction Disease Mgmt. Protocol AdherenceOrder Set Compliance Episode Profiling
Medication Errors Risk Scoring
Provider Performance Activity Based Costing
OperationalCare 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
20%
• Process of Care• Measures
related to CHF, Pneumonia, Surgical Improvement
30%
• Patient Experience • Communication,
responsiveness, Pain mgmt, cleanliness
20%• Efficiency• Cost per beneficiary
30%• Outcomes: • HFA’s, Mortality, Patient Safety
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
More costly group!!!
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 has analytic capability
$
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
Contin
uum
of K
now
ledge D
iscove
ry
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 managementPatient 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 dataGIS 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