Data Integration in Modern Health Care

Tom M Pettersson, Clinical Biochemist, PhD, Independent Senior Adviser,

LeanLabMed Elvar Theodorsson, MD, Professor,

Neurochemistry, LiU

20150305

Enabled by IT/ICT/LIMS TECHNOLOGY and SYSTEM

INTEGRATION

Tom M Pettersson LeanLabMed- 20150305

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3 Trends Digital Convergence – drives Learning Convergence of Sciences – drives Innovation New Biology – impacts Medical Practise Shift in Focus Predict, Prevent, Personalize, Participatory Conceptual Modelling Lean Healthcare Value Based Healthcare

Tom M Pettersson LeanLabMed- 20150305

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Swedish HC; Present, Future, Benchmark

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The HealthCare Continuum and

Focus

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Industrial Quality

and Management

Maturity Socratic direction ”Dialogue”

2 B Franklin ”No waste”

3 F Taylor ”Scientific

Management”

4 H Ford ”Assembly line –

Process for Mass-production”

5 E Deming – Total Quality

Management”

6 J Juran ”The

Quality Triangle”

7 Toyoda-Toyota Production

System”

8 Womack/Jones

Lean Production

Lean Services

9 B Smith,

“Six Sigma”

10 JH Holland

”CAS Biology

Systems Thinking”

11 “CEN-ISO

regulation”

Global/Individual

Patient need of

Medical Cure

Global/Individual

Patient need of

Health - Wellness

Academia

R & D for

New Biology

Knowledge

Medical

Applications

Academia

Education for

Adequate

Practice

Focus on Relevant

Knowledge, Dismiss

Irrelevant Knowledge

Pharma

Industry

Medical Devise

Industry

In Vitro

Diagnostic

Industry

IT/ICT/IST

Indsutry

Health

Wellness

Industry

Understanding the Big Picture and Needs Identify relevant Problems to Solve

Base Decisions on Data

Add Resources

Act

Waste

Ou

t

Waste

Out

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2 3

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6 7 8

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Lean Design for individual Patient Flow

Industrial Input Mature Quality Management (Lean Six Sigma ISO)

Learning and R&D in Academia

Integrated Learning in Diagnostic Services

Collaborative Learning in Healthcare (Purpose People, Processes)

Participating Patient – Medical Cure Maintaining Individual Health and Wellness Value Proposition

Medical and Operational Excellence, Satisfied Patients, Staff, Third Party Payer, Business Owner

Waste Out

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9 Waste Out

People Data Learning Flow

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Organize for Timeliness and Flow

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Common

Purpose

Lean Healthcare Patient Experience/Flow/Security/Medical-,Operational Excellence

Customer/

Patient level

Radiology

”Imaging” Physiol. Laboratory Medicine

Tissues

Cells

Micro-

Organisms

Biological

Fluids

Molecules

Nucleic

acids

Medical Diagnostics Services Integrated

Lead, Improve, Innovate, Transform, Drive Change

Service

level

Integrated

Information

Integrated Production

Level

Integrated

Scientific

Medical

Knowledge

Base

Skills

Develope the Medical Diagnostics Knowledge Worker

through Collaboration and Shared Purpose

Core, Rapid Response, POC Functions

Integrated

Production

Multidisciplinary Diagnostic Team Competences

Diagnostic, Scientific, Operational, Managerial

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Identify real need

Common customer service

Integrated information flow

Patient and sample material production processes Competence profiles, Diagnostics, Science, Operations, Leadership/Management Improvement and Learning Culture Common Values, shared Purpose and Knowledge

Integrate and Connect LabMed/MedDia

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Value Stream Design Quality Data Collection

No Data Quality without

Sample Quality (IT supported)

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Present Dilemma is Data Fragmentation – “Silos” Solution is Module based Design for Seamless Data Flow

HIS EPR

RIS Pacs Digital Pathology

Middle Wares Automated Process Control

Radiology/Physiology Pathology

Microbiology Clinical Chemistry Molecular Biology

RaR

Microbiology Clinical Chemistry Transfusion Med

Patient Access

IVD Industry provided Functions/Instruments

Standardized Coding; Snomed, NPU-coding, DRG, HL7, DICOM Standardized Labelling: Barcode (1D, 2D), RFID, personal ID (personnummer)

Add on IT Tools QC, QMS, Data Mining (Dashboards), Billing Telemedicine

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ICT for Bridging Biology and Medicine “The Omic Revolution”

Med DB

Med Res

Clinical Res

Bio DB Exposome Signs Symptoms Genome Epigenome Microbiome

New Skill Sets Bio-informaticians Translational Scientist Health Care Teams “MDT”

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Big HC Data Extraction

and Analysis

Stakeholder access

Quality Registers (70 started in Sw) and Bio

Banks creates massive of data)

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www Net working and Learning

Cross Disciplines Hospitals Patient Groups Regions Countries Cultures

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Healthcare is massively better today because of doctors, not technology “Once we’ve recognized the recipe for really great performance, the second thing we’ve discovered is that our most important resource for improving the ability of teams to follow through on those really critical things is data. Information is our most valuable resource, yet we treat it like a byproduct. The systems we have—Epic and our other systems—are not particularly useful right now in helping us execute on these objectives. We’re having to build systems around those systems”

Atul Gawande (author of Checklist Manifesto) …..from discussion on

It is about People, Data and Learning

Digital Health | LinkedIn

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Thank You

Tom Pettersson | LinkedIn

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