Analytics: Build or Buy?

Bryan Dangott, MDVice Chair of Pathology Informatics and Analytics

East Carolina Universitydangottb@ecu.edu

DISCLOSURE

In the past 12 months, I have not had any significant financial interest or other relationship with the manufacturers of the products or providers of the services that will be discussed in my presentation.

Any commercial products or entities discussed in this presentation do not represent endorsement by the speaker and are used for informational purposes only.

Learning Objectives:

1. Compare and contrast in house analytics solutions with vendor packages and reference lab analytics

2. Determine what scenarios favor building in house solutions vs buying external offerings

3. Highlight scenarios with case based examples

4. Discuss challenges and pitfalls of various analytics solutions

Analytics Definition

• Using infrastructure, data repositories, software, and intelligent probing to prepare, measure and present data to provide insights for decision intelligence and monitoring

www.gartner.com/binaries/content/assets/events/keywords/catalyst/catus8/2017_planning_guide_for_data_analytics.pdf

Complexity

https://www.sqlservercentral.com/articles/data-science-for-sql-folks-leveraging-sql-and-r

Analytics Scope: What topics?Lab Data

Clinical Pathology

Anatomic Pathology

Molecular Pathology

EHR Data

ADT

Diagnoses

Notes

Vitals

Demographics

Allergies

Pharmacy Data

Formulary

Medication class

Rx Info

Allergies

Radiology Data

Images

Reports

Organizational metrics

Inpatient Facilities

Ambulatory Sites

Affiliated Institutions

Analytics scope: Common lab metrics

Turn around times

Volume

Cost

Staffing

Workflow

1. Clinician Order

2. Specimen Collect

3. Receive in Lab

4. Test Performed / Resulted

5. Clinician Review

Turn Around Time

Collection and

Transport

OrderCould be ER, inpatient, clinic,

or follow up.

Receive in Lab

ResultEach test has its own TAT profile

Lowest

Variability

Clinician Review

Plotly Collect to Receive

Plotly Receive to Result

Define Use Case

https://www.wordclouds.com/

?What

does it

mean?

Choosing Wisely - ASCP

http://www.choosingwisely.org/societies/american-society-for-clinical-pathology/

Example: Blood Use

Chemotherapy

GI bleed

Trauma

Adult vs Pediatric

Marrow disorder

Nutritional status

Cardiac Patients

Value Equation

Outcomes are very hard to benchmark

• Biologic heterogeneity

• Variable presentation timelines

http://www.healthcareitnews.com/sponsored-content/solving-healthcare-value-equation-0

Metric: Patient outcomes

• Length of Stay

• Quality of Life

• Disease free survival

• Readmission rate

• Admissions per year

Aug 2016 Sep Oct Nov Dec Jan 2017

Aug 15

Sep 30

DischargeDec 8

Transfusion

Nov 21

Lab

Oct 14

Clinic

Jan 24

Clinic

Broad reach

High impact / Actionable

Quantitative

High Volume (70% of data in the EMR)1,2

Low Cost (3-5% of healthcare spending)3

Demonstrating Value in Lab Data

1. 1. J Pathol Inform. 2010; 1: 3

2. 2. JALM | Frequency that laboratory Tests Influence Medical Decisions| 410–414 | 01:04 | January 2017

3. 3. Smith et al, Educating Medical Students in Laboratory Medicine, AJCP 2010; 133:533-542

Provider metrics

• RVUs per provider

• Test ordering patterns

• Comparisons within specialty

• Cases outstanding

• Stains ordered per case

Benchmarking Challenges

Case severity

Number of visits/cases

Presenting dx and hx

Analytics scope: Users/Uses

• Who will use the system

• What data sources do they need

• What is the current state

• What is the desired future state

• Who has access

Analyst

Research

Clinical

Executive

What is already in place/prepackaged?

• Reporting engine

– Crystal Reports

• Vendor supplied reports

• Excel Dashboards

• Web based

What new skills may be required?

• ETL

• Security

• Database queries

• Statistical analysis

• Scheduling and automation

• Graphical presentation

• Point and click UI

What needs to be done to address Gaps?

• Middleware

• Scripts

• Prepackaged Software

• Enterprise solutions

• Pre/post processing of data

Data Sources

- Visits / Admissions

- Patients

- Providers

- Medications

- Laboratory

- Pathology

- Radiology

- Problems

- Diagnoses

- Charges / Claims

Medical

-Legal

Data

Cost

Data

Outcomes

Data

Patient

Satisfaction

DataReimbursement

Data

Safety

Data

CDWClinical Data Warehouse

Clinical Data Warehouse

Regularly updated

Electronic repository

Integrates multiple data sources

Provides an integrated view of patient information

What should the output look like?

• Flat file

• Spreadsheets

• Stats and Tables

• Web interface

• Dashboards

• Graphics

Stakeholder Requirements

Halwani F, Li WC, Banerjee D, Lessard L, Amyot D, Michalowski W, Giffen R. A real-time dashboard for managing

pathology processes. J Pathol Inform 2016;7:24

Build: Example Dashboard (PowerJ)

Halwani F, Li WC, Banerjee D, Lessard L, Amyot D, Michalowski W, Giffen R. A real-time dashboard for managing

pathology processes. J Pathol Inform 2016;7:24

Barriers to Insightful Analytics

Technical

• Data stored in multiple places

• Not documented discretely

• Does the data exist in a format that can be queried

Financial

• Costs of implementation

• Costs of staffing

Organizational

• How is the data going to be used

• Data gatekeepers

Barriers to Insightful Analytics

• Different LIS vendors

• Different EHR vendors

• Different delivery systems

• Different methodologies

• Different implementations

Other challenges

• Metrics need to be clearly defined

– Money saved by the lab may inversely impact some common performance metrics: # tests/employee

• New technology and investments in the lab may have benefits in other areas

– Microbiology and Pharmacy

• Need to consider the big picture

Silos and Fragmentation

• Healthcare data is fragmented and exists in silos within institutional and often departmental boundaries

– Organizations within the same delivery system may have different priorities

– Various Software vendors

– Processes

– Data definitions

– Development efforts may be replicated across organizations

Build Advantages

• There are no constraints on the type of questions which may be asked

• Can customize to meet changing organizational needs

Build Disadvantages

• User login support• Security design and maintenance

• Database design and access

• Customer service

• Implementation

• Reporting output and adjustments

• Help desk team functions

• On going development

Internal development also entails

Build: Querying Data

Acquire

• Flat file

• Export

• ETL

• Crystal Reports

Organize

• MSSQL

• PostgreSQL

• MariaDB

• Hadoop

Analyze

• SQL

• Knime

• Excel

• Jupyter

• Python

• Scipy

• numpy

• R

Deliver

• Plotly

• Bokeh

Build vs Buy: What is the budget?

• Licensing fees

• Installation fee

• Hardware / server / cloud computing costs

• Training costs

• Maintenance fees

• Labor costs

Buy Advantages

• Vendors have domain expertise

• Experience with multiple vendors and sites

• Implementation time line is faster

• Support provided by vendor

• User interface is more developed

• Security protocols probably more robust

• Prepackaged queries make it easier to use

Potential Buy Disadvantages

• Gaps should be evaluated during an RFP

– Data may need to be exported to a third party (does not apply to all vendors)

– Solution may not support integration with EHR or other data

Vendor Perspectives: Core Business Strengths

Health care organizations

• Caring for patients

• Operations

• May lack core competencies for software development

Analytics Software Company

• Economies of scale - serving hundreds of sites

• Software design benefits from continuous development

• Spreading development cost

• Develop scalable solution

General Analytics Tools vs Lab Specific

• Qlik

• Tableau

• Alteryx

• SAS

General Analytics Software Packages

• Sunquest

• Visiun

• Viewics

Lab Specific Solutions

Reference Lab

Analytics portal

• TAT

• Provider metrics

• Benchmarking to other clients

May not be able to analyze beyond what is sent to the reference lab

• Clinical data

• Internal lab data

Consulting Vendor

• Large consulting vendors may analyze exported data– EHR

– Lab

– Cost

– Reimbursement

– Pharmacy

– Benchmarking• Internal

• External

General Trends

LabCorp

https://www.tableau.com/products/desktop#videoTableau

Qlik

Visiun

https://visiun.com/performance-insight/modules/peer-comparison-module/

Summary

• Define Analytics Scope

• Define the Users and Use cases

• Build vs Buy is really a spectrum

– How much to Build

• Dependent on site resources

– How much to Buy

• RFP

Credits/Contributors

• Phil Boyer, MD PhD• East Carolina University

• Peter Kragel, MD• East Carolina University

• Tim Bickley, MT(ASCP),MBA,CPHIMS• Visiun, VP Sales North America

• Paul Stinson• Sunquest, Chief Growth Officer

• Chris Garcia, MD• LabCorp, Medical Director Clinical Informatics

• Dave Swarr • Qlik, Account Manager

• Spencer Smith• Tableau, Business Development

• Erica Fowler• Roche, Commercial IT