+

The Missing Piece?Understanding Provider Organization Capabilities to Engage with the Learning Health System

Julia Adler-Milstein, PhDMarch 14, 2017

+Overview of Talk Setting the Context: Lessons from Other Industries

on IT Value

Knowledge Management as a Provider Organization Competency

Knowledge in the Era of Health Data Science

Implications for Provider Organizations and LHS Engagement

Lessons on Realizing Tech Value from Other Industries

David P. (1990) The Dynamo and The Computer.

Lessons on Realizing IT Value from Other Industries

5

“New technology takes time to have a big economic impact.

More importantly, businesses […] have to adapt before that will happen.”

http://www.slate.com/articles/arts/the_undercover_economist/2007/06/the_shock_of_the_new.html

Organizational Context

IT

Outcomes

Organizational contexts conducive to realizing IT value:o give frontline staff the authority and accountability

to make decisions based on newly available, real-time data,

o and ensure they have the training and skills to do so

Lessons on Realizing IT Value from Other Industries

e.g., Brynjolfsson E, Hitt L. (1996). Firm-level Evidence on the Returns to Information Systems Spending. Management Science.

Healthcare

The EHR-Performance Gap On the one hand…

o Early studies from individual institutions reveal substantial quality and efficiency gains from EHRs

Served as the motivation for HITECH

On the other hand…o Recent, large-scale studies fail to find a consistent

relationship between EHR adoption and improved performance (e.g., Appari 2012, Adler-Milstein et al. 2013)

+ MAGICAL THINKING

REALITY

IT Better Performance

ITBetter

PerformanceComplementary

Organizational Changes

+Overview of Talk Setting the Context: Lessons from Other Industries

on IT Value

Knowledge Management as a Provider Organization Competency

Knowledge in the Era of Health Data Science

Implications for Provider Organizations and LHS Engagement

+What is knowledge? What is knowledge management? In the context of this talk…

Knowledge about health and healthcare that is generated outside the practice setting (provider organization)

Knowledge management is the dynamic set of organizational capabilities needed to convert knowledge to practice

+Current State

Lack consensus on KM competencies And how they need to adapt as external

knowledge and knowledge dissemination infrastructures change

Lack data on current state of KM in US provider organizations

Can look to leading provider organizations for examples

+Knowledge Management: Strategic Goals

Reduce the cost and increase the speed of knowledge acquisition and maintenance for decision support

Speed translation of clinical innovation and evidence into clinical practice

Proactive, anticipatory decision support architecture Improve organizational effectiveness as a learning

organization through organizational alignment and data-driven performance improvement

Knowledge management as decision support

Adapted from T. Hongsermeier

+Content Life-Cycle Challenges:

Committee, Department, Researcher, or Other

Proposes to Implement Content

Guideline is Defined and Validated

Functional Knowledge SpecificationFor Encoding is

Designed and Validated

Ongoing Revisions or Eventual Sunset

Of Encoded Guideline

•Prioritization mechanism not always clear•Stewardship processes not always clear•Lack of coordination

•Unclear mechanism for subject matter expert participation…•No budgetary model to reimburse experts… •No tools to support efficient collaboration•Little or no audit trail of decisions made

•Project competition with other engineering projects, prioritization processes unclear•Knowledge editors typically do not enable content auditing, knowledge editors siloed, no support of inheritance or propagation•Little or no documentation about content in production •MS Office doesn’t help maintain data about content•Little analytic data available on decision support content orimpact on clinical outcomes impact to direct updating•Tendency to rely on query of transaction systems•No content management tools to support process and ensure timeliness

Specification isEngineered into Production Generating

a Technical Specification

Adapted from T. Hongsermeier

+Evolving knowledge management infrastructure:

Knowledge Management Generation 1:• Build and deploy a document library to provide enterprise wide access

to specifications of decision support knowledge• Inventory all structured knowledge in production• Create and develop a knowledge repository

Knowledge Management Generation 2:• Implement tools to support collaborative content consensus, iterative

drafting of guidelines and conversion to functional knowledge specifications

• Knowledge repository expanded to support browsing of pre-production and “in-production” knowledge

• Implement tools to support content management processes using lifecycles and workflows (knowledge maintenance)

Knowledge Management Generation 3:• Integrate legacy and new content authoring tools with content

management infrastructure (knowledge editing)Adapted from T. Hongsermeier

+Current State

KM not a recognized “competency” of provider organizations

Where KM is occurring, it is mostly focused on: Proving access to external information resources Deciding what should decision support should include

Substantial disparities in KM capabilities by type of provider organization

+Two observations

The huge investment in 21st century health knowledge generation has not been coupled with investment in 21st century knowledge application.

It is critical to anticipate how knowledge will change, and how healthcare delivery organizations will need to adapt.              

+Overview of Talk Setting the Context: Lessons from Other Industries

on IT Value

Knowledge Management as a Provider Organization Competency

Knowledge in the Era of Health Data Science

Implications for Provider Organizations and LHS Engagement

+Knowledge will:Come in more forms and at different levels of scale (i.e., individual, population)Be regularly changing and updating

Be inherently probabilistic

Be customizable to specific people and situationsCome via more channels (i.e., beyond journals and guidelines)Come from a variety of sourcesBe more accessible

Be a recognized “entity”

+Overview of Talk Setting the Context: Lessons from Other Industries

on IT Value

Knowledge Management as a Provider Organization Competency

Knowledge in the Era of Health Data Science

Implications for Provider Organizations and LHS Engagement

+Knowledge will: Which will require

healthcare delivery organizations to:

Come in more forms and at different levels of scale (i.e., individual, population)

Have a process for “local” translation and operationalization

+Knowledge will: Which will require

healthcare delivery organizations to:

Be regularly changing and updating

Have a rapid process for decision-making about fit/relevance Have frontline work processes that can continuously adapt

+Knowledge will: Which will require

healthcare delivery organizations to:

Be inherently probabilistic

Have a workforce that can make decisions under conditions of uncertainty

+Knowledge will: Which will require

healthcare delivery organizations to:

Be customizable to specific people and situations

Have infrastructure for mass customization

+Knowledge will: Which will require

healthcare delivery organizations to:

Come via more channels (i.e., beyond journals and guidelines)

Have varied mechanisms of receipt

+Knowledge will: Which will require

healthcare delivery organizations to:

Come from a variety of sources

Have a process to validate and trust

+Knowledge will: Which will require

healthcare delivery organizations to:

Be more accessible

Have a workforce and work processes that enable direct access to role-relevant knowledge

+Knowledge will: Which will require

healthcare delivery organizations to:

Be a recognized “entity”

Have a governance process for adopted knowledge  

+Knowledge will: Which will require

provider organizations to:Come in more forms and at different levels of scale (i.e., individual, population)

Have a process for “local” translation and operationalization

Be regularly changing and updating Have a rapid process for decision-making about fit/relevance Have frontline work processes that can continuously adapt

Be inherently probabilistic Have a workforce that can make decisions under conditions of uncertainty

Be customizable to specific people and situations

Have infrastructure for mass customization

Come via more channels (i.e., beyond journals and guidelines)

Have varied mechanisms of receipt

Come from a variety of sources Have a process to validate and trustBe more accessible Have a workforce and work processes

that enable direct access to role-relevant knowledge

Be a recognized “entity” Have a governance process for adopted knowledge  

+What do we know about how to do these things well?Tidbits like… Healthcare delivery organizations will need a workforce that

includes those with familiar titles (e.g., “doctor” and “nurse”) but new skillsets, alongside people with entirely new roles.

These new skillsets and roles are beginning to be conceptualized and articulated in the form of competencies that include: (1) knowing what you do and don’t know, (2) ability to ask a good question, and (3) skills in evaluating and weighing evidence.

Yet there is no guidance for healthcare delivery organizations in terms of how to increase these competencies in their workforce.

+What do we know about how to do these things well?Literature from other industries that are ahead of healthcare in their data science maturity Point to the need to put information and the relevant decision

rights in the same location. Specifically, when information is created and transferred, and

expertise is often not where it used to be, an organization needs to be flexible enough to minimize the “not invented here” syndrome and maximize cross-functional cooperation.

Also a need to shift the culture of an organization to one in which the first question is not “What do we think?” but “What do we know?” as well as “Where did the data come from?”, “What kinds of analyses were conducted?” and “How confident are we in the results?”

+Concluding Thoughts

Nascent state of understanding about how knowledge characteristics will change, how healthcare delivery organizations will need

to change in response, how to execute those changes

Such understanding is necessary to bridge the “last mile” of the learning health system.

It is therefore imperative to begin the process of discovery.