from practice-based evidence to evidence-based practice …€¦ · evidence-based practice in...

From Practice-Based Evidence to Evidence-Based Practice in

Seventeen Syllables

Five Days in May, 2016

May 31, 2016 Kenneth Moselle, PhD, R.Psych. Enterprise Information Architect

IHealth Lead – Applied Research

Health Analytics/Research

8

Forewarned is forearmed.

I am so discreet But haiku can be direct So please be prepared.

Careful!! Rhetorically Sharp Edged Comments

Focus today is on Chronic Disease – not ALL disease, illness & suffering.

Problem

Research happening Lots of money being spent Where’s the improvement?

E.g, “Increasing impatience with the pace at which scientific discovery results in new products/interventions”. Reference: 5 Days in May presentation

Solution –The Five Eight Right Applied Research Actions

Linked data products If the path is circular We still need a map

Right Model Exemplifying

the Problem Right Problem

Right Research

Design , Right

Statistical Models

Right Data Right Data

Management

Right Execution of

Statistical Analyses

Right Interpretation of Statistical

ResultsRight Application of Statistically-

Derived Products

Right Evidence-

Based Stewardship

Right Care & Feeding of Statistical

Models

Evidence Based Practice from Practice-

Based Evidence

Moselle, May 31, 2016

The 3T’s Research It’s forcing the agenda We think it’s strategic

Specific Context – Typologies, Trajectories & Transitions in Mental Health & Substance Use (Island Health Applied Research Partnership with Cerner Math Research Unit and UVIC Institute on Aging & Lifelong Health

Drowning, an ocean Of big data marketing

Cutting through buzzwords

The First Ring – Right Models, Right Scenarios, Right Patient Stories

Let’s use health data To tell stories of patients

With chronic disease



Right Research

Design , Right

Statistical Models


Management

Right Execution of




Derived Products

Right Evidence-

Based Stewardship


Models


Based Evidence

BUT WAIT!

For what is about to happen, we have to be in right frame of mind.

Think “line segments with slope & shape” – and because these are line segments, not lines, they also have starting points and ending points.

Behavioural Interventions

Primary Care

Genetic Factors (Genome)

Marijuana daily

Cutting classes

Binge drinking - weekends

Diagnosis: 315.39 Other developmental

or speech disorderDiagnosis: F81.0 Specific reading

disorder

Diagnosis: F90.0 ADHD, predominantly

inattentive type

Diagnosis: F21 Schizotypal disorder

Psychiatric Emergency Svs (ED)

Psychiatric Intensive Care

Psychiatric Acute Care

Medical ED

Health Risk Profile: Smoking

MarijuanaNeuropsychological

Evaluation

Supported Apartment

Mobile Crisis Response TeamDetox

Post-Withdrawal StabilizationSupported Apartments

Diagnosis: T43.6255 Adverse effect of amphetamine

Diagnosis: F20 Schizophrenia

Diagnosis: B20 HIV Disease

Diagnosis: E88.81 Metabolic Syndrome

Case Management – Assertive Community

Treatment

Disordered, peculiar language, delusional

content


Consultation - Pediatrician

Reckless, impulsive, aggressive behaviour

in schoolEnvironmental Impacts on Gene

ExpressionNeurbiology

Other ‘omic’ factors

Limited reading ability by end of Grade 2

Prescribed Ritalin

Associating with drug-using peer

group age 13

GP calls Police/Mobile Crisis Response Team

Early Psychosis Initiative (EPI)

Prescribed Antipsychotic Medication

Limited Medication Adherence

Return Home

Police/Mobile Crisis Response Team


Medical ED

Methamphetamine - Intermittent

Police

Case Manager, ‘Ordinary’ Schizophrenia Svs


PoliceMedical ED


Homeless

Residential Care Facility

Injection use, methamphetamine




Alcohol abuse - dailyInjection drug use –

sharing needles

Nutritionally compromised

Exposure to various infectious illnesses

Homeless

Agitated psychosis, fight in shelter

Medical EDLab Tests

Market Rent Housing

Good Medication AdherenceRoutine Labs

Acute vs Chronic Illness – From a Geometric Standpoint • Acute Illness has “severity” and is ‘bounded’ (‘cured’ or left with some form of disability) • Chronic Illness has an indefinite extension and a

‘Momentum’ that we would like to describe in terms of a ‘line’ or ‘curve’ (over time) ▫ The slope and shape of the curve says something

about the modifiability or treatability of the condition, and timing/prognosis.

▫ Discontinuities in the shape of the curve says something about ‘new’ clinical factors entering the picture, and/or external factors having an impact.

Minor Illness

Really Bad Illness

Different Chronic Conditions Stable Over Time (Slope = 0) Steadily Deteriorating (Slope = -X)

‘Things’ happening

Want to change the things? You have to find the causes,

And then change THOSE things! (Galen)

Stable, then a series of more rapid declines

Rich scenarios. Examples are the go-cart.

Kant really said that!

A person with a chronic/severe Mental Health & Substance Use (MHSU) problem


Primary Care


Marijuana daily

Cutting classes




disorder


inattentive type





Medical ED



Evaluation

Supported Apartment








Treatment


content








Prescribed Ritalin


group age 13





Return Home



Medical ED


Police



PoliceMedical ED


Homeless







sharing needles



Homeless


Medical EDLab Tests

Market Rent Housing


Early Warning Signs and Prodromal Phase


Primary Care


Marijuana daily

Cutting classes




disorder


inattentive type





Medical ED



Evaluation

Supported Apartment








Treatment


content








Prescribed Ritalin


group age 13





Return Home



Medical ED


Police



PoliceMedical ED


Homeless







sharing needles



Homeless


Medical EDLab Tests

Market Rent Housing


Mid-Course – Reaching the Clinical/Functional/Behaviour ‘Tipping Point’


Primary Care


Marijuana daily

Cutting classes




disorder


inattentive type





Medical ED



Evaluation

Supported Apartment








Treatment


content








Prescribed Ritalin


group age 13





Return Home



Medical ED


Police



PoliceMedical ED


Homeless







sharing needles



Homeless


Medical EDLab Tests

Market Rent Housing


Descending Under the Force of Clinical Gravity, But Can a Crash Landing be Avoided?


Primary Care


Marijuana daily

Cutting classes




disorder


inattentive type





Medical ED



Evaluation

Supported Apartment








Treatment


content








Prescribed Ritalin


group age 13





Return Home



Medical ED


Police



PoliceMedical ED


Homeless







sharing needles



Homeless


Medical EDLab Tests

Market Rent Housing


MHSU – Person-as-Diagnoses Over Time


Primary Care


Marijuana daily

Cutting classes




disorder


inattentive type





Medical ED



Evaluation

Supported Apartment








Treatment


content








Prescribed Ritalin


group age 13





Return Home



Medical ED


Police



PoliceMedical ED


Homeless







sharing needles



Homeless


Medical EDLab Tests

Market Rent Housing


Schizophrenia. Co-morbid substance

abuse. That’s just for starters.

Person as a Series of Linked Longitudinal Encounters with the Service System[Treatments, Interventions]


Primary Care


Marijuana daily

Cutting classes




disorder


inattentive type





Medical ED



Evaluation

Supported Apartment








Treatment


content








Prescribed Ritalin


group age 13

How late is too late. How early is too early?





Return Home



Medical ED


Police



PoliceMedical ED


Homeless







sharing needles



Homeless


Medical EDLab Tests

Market Rent Housing


Tons of services If only I took my meds

I would be cured??

MHSU – Health Risk/Promotion Perspective [Proximal Determinants of Health Perspective]


Primary Care


Marijuana daily

Cutting classes




disorder


inattentive type





Medical ED



Evaluation

Supported Apartment








Treatment


content








Prescribed Ritalin


group age 13





Return Home



Medical ED


Police



PoliceMedical ED


Homeless







sharing needles



Homeless


Medical EDLab Tests

Market Rent Housing


Serious, Severe MHSU – Inexorable Downward Socio-Economic Mobility – And Essentially Everything Bad That Goes Along with That

I am now quite poor. Though I harbor delusions

My hunger is real.

Over time: Progressive loss of educational opportunities or employment opportunities and social stigma and social exclusion and loss of personal freedom and loss of family and loss of children and engagement with the criminal justice system and…

Distal (non-medical determinants of health):

Downward Socio-Economic Mobility

Costs - Care pathway that does not provide best possible service until late in the trajectory – Pay Now/Pay Later


Primary Care


Marijuana daily

Cutting classes




disorder


inattentive type





Medical ED



Evaluation

Supported Apartment








Treatment


content








Prescribed Ritalin


group age 13





Return Home



Medical ED


Police



PoliceMedical ED


Homeless







sharing needles



Homeless


Medical EDLab Tests

Market Rent Housing


$

$ $

$ $$ $$

$$

$$$$$

$ $ $ $

$ $

$ $ $ $ $ $

$$

$$

$

$$

$ $

$$

$ $$

$$ $

$

$

$

$

$ $$

$

$

$

$

$$

$

$

$$

Possible alternative care pathways


Primary Care


Marijuana daily

Cutting classes




disorder


inattentive type





Medical ED



Evaluation

Supported Apartment








Treatment


content








Prescribed Ritalin


group age 13





Return Home



Medical ED


Police



PoliceMedical ED


Homeless







sharing needles



Homeless


Medical EDLab Tests

Market Rent Housing


Too early – the trajectory is still propelled by patient/client behaviour


Primary Care


Marijuana daily

Cutting classes




disorder


inattentive type





Medical ED



Evaluation

Supported Apartment







Case Management – Assertive Community Treatment


content








Prescribed Ritalin


group age 13




Return Home



Medical ED


Police



PoliceMedical ED


Homeless







sharing needles



Homeless


Medical EDLab Tests

Market Rent Housing


Case Management – Assertive Community Treatment

$$

$$

$$

$

Best possible timing for best-possible service model [Assertive Community Treatment]


Primary Care


Marijuana daily

Cutting classes




disorder


inattentive type





Medical ED



Evaluation




Treatment


content








Prescribed Ritalin


group age 13





Return Home



Medical ED


Psychiatric Day Hospital

Various Rehab/Recovery Services

Market Rent Housing

Good Medication Adherence

Marijuana daily

$ $ $ $$ $ $

“If you are not progressing along the true way, a slight twist in the mind can become a major twist. This must be pondered well.” ― Miyamoto Musashi, The Complete Book of Five Rings

https://www.goodreads.com/author/show/8330589.Miyamoto_Musashi

https://www.goodreads.com/work/quotes/1318780



A Person with Type II Diabetes – Over Time

Distal (non-medical) Determinants:

Racism, Social Exclusion

Distal (non-medical) Determinants: Limited Education

Risk Factors: Insulin Resistance, Child or

Mother Nutrition, Post-Natal

Clinical Problem/Warning Sign: Baby Bottle Tooth Decay

Elevated BMI


Routine Lab Work

Diet

Reduced Capacity to Regulate Emotions and

Behaviour

Clinical Problems

Diagnosis: E11.9 Type II diabetes mellitus

without complications

Children’s Oral Health Initiative

(FNIHB)

Hospital ED

Diabetes Nurse Educator

Diagnosis: E11.69 Diabetes mellitus with

ketoacidotic coma

Diagnosis: E11.29 Diabetes mellitus with other diabetic kidney complications

Diagnosis: E11.59 Diabetes mellitus with other circulatory complications

Med/Surg Acute Care

Renal Dialysis

Palliative Care

Risk Factors: Gestational Diabetes

Home & Community Care

Extended Care


Gene

tic F

acto

rs (G

enom

e)

Prescribed Insulin

Distal (non-medical) Determinants: Limited Employment Opportunities

Distal (non-medical) Determinants: Food Insecurity

Distal (non-medical) Determinants: Preservation of

Language and Culture

Epig

entic

/Om

ic La

ndsc

ape

Physical Activity

Healthy Pregnancy & Early Infancy (FNIHB)

Diet

Physical Activity

Drops out of school

Poor-paying job

Alcohol abuse

Smoking

DietPhysical Activity

Alcohol abuse

Smoking

Unemployed

Hospital ED

Limited Treatment Adherence

Prescribed metformin

Limited Treatment AdherenceCommunity Dietician


Severe Psychiatric Illness Nutrition; Antipsychotic Medications

So – what are the causes/determinants of these trajectories/components of trajectories?

Dynamics inherent in illness

(clinical dynamics)

The person’s ‘will’ and behaviour

Functions performed by service system


Primary Care


Marijuana daily

Cutting classes




disorder


inattentive type





Medical ED



Evaluation

Supported Apartment








Treatment


content








Prescribed Ritalin


group age 13






Return Home



Medical ED


Police



PoliceMedical ED


Homeless







sharing needles



Homeless


Medical EDLab Tests

Market Rent Housing



Primary Care


Marijuana daily

Cutting classes




disorder


inattentive type





Medical ED



Evaluation

Supported Apartment








Treatment


content








Prescribed Ritalin


group age 13





Return Home



Medical ED


Police



PoliceMedical ED


Homeless







sharing needles



Homeless


Medical EDLab Tests

Market Rent Housing



Primary Care


Marijuana daily

Cutting classes




disorder


inattentive type





Medical ED



Evaluation

Supported Apartment








Treatment


content








Prescribed Ritalin


group age 13





Return Home



Medical ED


Police



PoliceMedical ED


Homeless







sharing needles



Homeless


Medical EDLab Tests

Market Rent Housing


Distal (non-medical determinants of

health)

This gives us our First Principle for Applied Clinical Research (in the area of Chronic Disease)

Principle #1: In research or evidence-based QA/QI concerned with chronic disease, don’t get reductionist about things. Note: we will be seriously happy that we observed this first principle when our efforts to product clinically actionable knowledge require the use statistical methods that can approximate the ‘certainty’ of randomized controlled trials without random assignment of people to treatments (i.e., the status quo for EHR-based research) – ONLY IF THE WORKING MODELS ARE RELATIVELY ‘COMPLETE’.

Some Research Questions Embedded Right in the Middle of these Scenarios

• Are there real longitudinal types? Are these within-person-over-time stories truly scenarios or idiosyncratic sequences of events?

• Can we create a typology [taxonomy] expressed in trajectories rather than point-in-time constellations of symptoms and signs?

• Many people look similar in the earlier stages and very different in the later stages. Do some trajectories branch? What causes some people to head in a healthy direction? What causes some people to walk a path of darkness?

• How late is too late to alter trajectories? • How early is too early?

Could it be that something as fundamental as our current system for classifying diseases is actually inhibiting progress?*

*Committee on the Framework for Developing a New Taxonomy of Disease, 2011

The Second Ring – Right Research Designs, Right Statistical Methods Right Model

Exemplifying the Problem Right Problem

Right Research

Design , Right

Statistical Models


Management

Right Execution of




Derived Products

Right Evidence-

Based Stewardship


Models


Based Evidence

Moselle, May 31, 2016People are stories:

Within-person-over-time. Our stats should mirror

Randomization Is not what we do when we

Treat individuals

RCT's are nice When people are no more than

Core signs and symptoms

Before EHR Must randomize to control

Now we can measure

If we want to do useful research, we need to start by getting clear on what we mean by “outcomes” as experienced by patients/clients.


Primary Care


Marijuana daily

Cutting classes




disorder


inattentive type





Medical ED



Evaluation

Supported Apartment








Treatment


content








Prescribed Ritalin


group age 13





Return Home



Medical ED


Police



PoliceMedical ED


Homeless







sharing needles



Homeless


Medical EDLab Tests

Market Rent Housing


Answer A: Outcome expressed in diagnostic terms (e.g.,

cure rates)


Primary Care


Marijuana daily

Cutting classes




disorder


inattentive type





Medical ED



Evaluation

Supported Apartment








Treatment


content








Prescribed Ritalin


group age 13





Return Home



Medical ED


Police



PoliceMedical ED


Homeless







sharing needles



Homeless


Medical EDLab Tests

Market Rent Housing


Answer B: How about behaviour? Behavioural

Interventions

Primary Care


Marijuana daily

Cutting classes




disorder


inattentive type





Medical ED



Evaluation

Supported Apartment








Treatment


content








Prescribed Ritalin


group age 13






Return Home



Medical ED


Police



PoliceMedical ED


Homeless







sharing needles



Homeless


Medical EDLab Tests

Market Rent Housing


Answer C: How about: changes in service system utilization (and associated costs)?

How about all of the above, and what would that MEAN? What would it look like?

$ $ $ $$ $ $


Primary Care


Marijuana daily

Cutting classes




disorder


inattentive type





Medical ED



Evaluation




Treatment


content








Prescribed Ritalin


group age 13





Return Home



Medical ED




Market Rent Housing


Marijuana daily

$ $ $ $$ $ $$ $ $ $

$ $ $

$ $ $

$ $ $

$

$

Outcomes within a non-reductionistic (i.e. personally meaningful) within-person-over-time perspective.

Initial Phases of Trajectory

Middle Phases – Actively Engaged with Various

Components of the Service System – While Condition

Continues to Evolve

Later Phases in Trajectory – ‘Outcomes’


Primary Care


Marijuana daily

Cutting classes




disorder


inattentive type





Medical ED



Evaluation




Treatment


content








Prescribed Ritalin


group age 13





Return Home



Medical ED




Market Rent Housing


Marijuana daily


Primary Care


Marijuana daily

Cutting classes




disorder


inattentive type





Medical ED



Evaluation




Treatment


content








Prescribed Ritalin


group age 13





Return Home



Medical ED




Market Rent Housing


Marijuana daily

VS VS

Service Model A

Service Model B

Ring #3 – Right Data, Right Data Management



Right Research

Design , Right

Statistical Models


Management

Right Execution of




Derived Products

Right Evidence-

Based Stewardship


Models


Based Evidence

Display all your charms Make friends with distant countries

Let the guest play host

Palette #1 – ‘Facts’ or attributes or characteristics of the person


Primary Care


Marijuana daily

Cutting classes




disorder


inattentive type





Medical ED



Evaluation

Supported Apartment








Treatment


content




in school Environmental Impacts on Gene




Prescribed Ritalin


group age 13





Return Home



Medical ED


Police



PoliceMedical ED


Homeless







sharing needles



Homeless


Medical EDLab Tests

Market Rent Housing


Distal (non-medical) Determinants:


Distal (non-medical) Determinants: Limited Education

Risk Factors: Insulin Resistance, Child or

Mother Nutrition, Post-Natal

Clinical Problem/Warning Sign: Baby Bottle Tooth Decay

Elevated BMI


Routine Lab Work

Diet

Reduced Capacity to Regulate Emotions and

Behaviour

Clinical Problems

Diagnosis: E11.9 Type II diabetes mellitus

without complications

Children’s Oral Health Initiative

(FNIHB)

Hospital ED

Diabetes Nurse Educator

Diagnosis: E11.69 Diabetes mellitus with

ketoacidotic coma

Diagnosis: E11.29 Diabetes mellitus with other diabetic kidney complications

Diagnosis: E11.59 Diabetes mellitus with other circulatory complications

Med/Surg Acute Care

Renal Dialysis

Palliative Care

Risk Factors: Gestational Diabetes

Home & Community Care

Extended Care


Gene

tic F

acto

rs (G

enom

e)

Prescribed Insulin

Distal (non-medical) Determinants: Limited Employment Opportunities

Distal (non-medical) Determinants: Food Insecurity

Distal (non-medical) Determinants: Preservation of

Language and Culture

Epig

entic

/Om

ic La

ndsc

ape

Physical Activity

Healthy Pregnancy & Early Infancy (FNIHB)

Diet

Physical Activity

Drops out of school

Poor-paying job

Alcohol abuse

Smoking

DietPhysical Activity

Alcohol abuse

Smoking

Unemployed

Hospital ED

Limited Treatment Adherence

Prescribed metformin

Limited Treatment AdherenceCommunity Dietician


MHSU

Type II Diabetes

Palette #2 – [Quasi-] Modifiable Factors: Patient/Client Behaviour

Linked Longitudinal Encounter Data? • Person over time in relationship to programs – and

the services they provide – an embodiment of trajectories

• The interaction of Service Models and Real People. • Indicator of effectiveness of Service Models. • Context for clinical ‘facts’ about the patient, e.g.,

Severity of Psychosis on admission to ED; Severity of Psychosis at time of Discharge from Acute Care….

Do normal people Say “Linked Longitudinal

Encounter Data”?

Palette #3 – The Contexts: Patient/Client as ENCOUNTERS with Service System Locations

In the EHR, all of the ‘facts’ about the person in Palette #1 are associated with an Encounter with a Service Location.

The Dark (as in Non-Transparent) Side of Transactional Data in the EHR

This is what you actually will SEE when you get your file filled with encounter

data!

The Five W’s related to Clinical/Transactional Data – Who, Who, Who, Who, and Who

Who can supply those transactional data to researchers – in such a way that they understand what they are looking at? • Who knows what those locations are? • Who knows what they WERE? • Who knows who is seen in those locations? • Who knows who works in those locations and what they do? • Who must participate in the research, throughout the lifecycle

of the research, to make sure that results that are expressed in terms of these locations have been interpreted correctly??

Data Platform Essentials: It’s not just getting the data. It’s getting the RIGHT data and knowing what you have.

• “You know what you want. I know what you need.” (Jimi Hendrix, PhD, Data Scientist)

• “You know what you want. I know what we have.” (Data Custodian, Data Architect)

• “You know what we have. I know what it means.” (Data Curator).

• If researchers want to use high complexity/low transparency data that they have not collected – they must engage with Data Custodians/Architects AND Data Curators

Palette #1 – ‘Facts’ or attributes of the person – several sources

NON-MEDICAL Determinants of Health

Patient/Client Behaviour & Preferences

Electronic Health Record

Bench Scientists, Lab – ‘omic’ data

Clinical/ Transactional

Data Administrative Data

Administrative Data

Clinical/ Transaction

al Data

Two more principles for Applied Clinical Research • Principle #2 – Data Transparency – whoever provides

you with the data must also be able to render the data transparent with regard to meaning (i.e., the people and processes that were involved in the data coming to life).

• Principle #3 – Proximity – when it comes to transactional data sets – you must go to sources that are proximal to the points where services are delivered.

Location data Inherently localized

Must go to the source

Talk “data platform”? Lets start by talking data.

What’s getting platformed?

What types of data Are suited to a platform?

And what will roll off?

Secure Research Environments Integrating Data Transparency Functions

Technical Controls

Administrative Controls

Data Architecture, Linkage, De-Identification

(Statistical Disclosure Control)

Secure Research Environment

Technical Controls




Secure & Data- Meaning-Transparent Research Environment

Data Curator Functions

Island Health Secure Research Environment

Health Authority Data

Technical Controls




Secure & Data- Meaning-Transparent Research Environment

Data Curator Functions

Interoperable SRE’s = ‘Fractal’ Data Platform

Ring #6 – Right Translation of Knowledge into Practice



Right Research

Design , Right

Statistical Models


Management

Right Execution of




Derived Products

Right Evidence-

Based Stewardship


Models


Based Evidence


Reports, Dashboards for Clinical Stewards and QA/QI Councils

What interventions/integrated packages of interventions work for people at different stages in different trajectories?

Trajectory-Based Types/Typology

Aggregate Views: Practice in Relationship to Outcomes for Trajectory-Based Cohorts: Who is using EHR-based tools to do what the data show is ‘best possible practice’ for a Trajectory-Type, and is this working as expected?

EHR/EMR-Based Clinical Decision Support Tools & ‘Smart’ Order Sets

What do good/poor outcomes look like for different phases of different trajectories?

Population Health – Incidence/Prevalence of Types within Populations; Strategic Plans Based on Scenario Testing in Treatment/Outcome Models

Policy; Funding Priorities – To Deliver Best Possible Services, Leveraging off of EHR-based clinical decision support keyed to Types

Knowledge Translation MechanismsResearch-Grade Knowledge KEY

Know what you’re building The truth can’t be too concrete

Know what you intend

Target Information Architecture – from EHR-Derived Evidence-Based Practice to Local Clinical Evidence-Based Strategic Planning










A layered model of functions and associated information requirements within a layered health service system.

Building houses? Who is going to live there In what neighborhood??

Get materials Before you have a blueprint???

So sorry, no roof!

Target Information Architecture???? • A structured description of clinical services/functions or oversight

or planning functions • Identification of persons/problems impacted by these functions • Contexts in which the functions are performed • Information required to perform functions in an optimal fashion • Information management technologies that deliver the

information. • Clear specification of what is getting optimized through

performance of the functions using the information. • Applied Clinical Research should be able to identify where, within

a clearly articulated Target Information Architecture, the research-derived information products will be ‘deposited’.

Foundation TIA Layer Targets – Front-Line Clinical Practice






Building off of First Layer – Clinical Evidence-Based QA/QI





Targeting the Point of Service, QA/QI Processes, and Evidence-Based Strategic Planning – and Funding




Extract from EHR New taxonomies, new types

New populations

Target Information Architecture (TIA) - sighted at inception of research that aspires to Knowledge Translation (KT)










KT’s true name is Applied clinical

research. Drive to TIA

The Crest-Jewel Applied Clinical Research Principle – Baking Knowledge Translation (KT) into Research with Target Information Architectures

• Principle #4 – Target Information Architectures - Derive research questions or evidence-based QA/QI questions from Target Information Architectures; derive & drive Knowledge Products back into Target Information Architectures

Strategy, but no Blueprint or building plan will

Sanction anything.

Strategy, but no Blueprint or building plan will

Sanction anything.




Summary – A Strategy for Generating Clinically Actionable Knowledge Products from Data Extracted/Abstracted from the EHR











Primary Care


Marijuana daily

Cutting classes




disorder


inattentive type





Medical ED



Evaluation

Supported Apartment








Treatment


content








Prescribed Ritalin


group age 13





Return Home



Medical ED


Police



PoliceMedical ED


Homeless







sharing needles



Homeless


Medical EDLab Tests

Market Rent Housing


The Person as Person

Person Trajectories Re-Expressed with

Data

Trajectory-Based Models of Risks, Interventions,

Outcomes

Research Designs that ‘Work’ with EHR Data


Primary Care


Marijuana daily

Cutting classes




disorder


inattentive type





Medical ED



Evaluation

Supported Apartment








Treatment


content








Prescribed Ritalin


group age 13






Return Home



Medical ED


Police



PoliceMedical ED


Homeless







sharing needles



Homeless


Medical EDLab Tests

Market Rent Housing


Target Information Architecture

“Step by step walk the thousand-mile road.” ― Miyamoto Musashi, The Book of Five Rings




from practice-based evidence to evidence-based practice …€¦ · evidence-based practice in...

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