EMR Implementation through the lens of Complexity Science

1
EMR Implementation through the lens of Complexity
Science

• Presented by Caleb Goodwin

The nature of the whole is always different from
the sum of its parts. Fritjof Capra, The Web of
Life
2
Goal of lecture

• From EMR failure to complexity to complexity
  approach for EMR implementation

3
EMR Failure A Case Study

• Case Study EMR failure at the San Francisco
  Department of Public Health
• Failure after two years
• Reason cited Vendor did not reflect the diverse
  services provided by the organization.

4
EMR Failure A Case Study

• Lack of human-centered design
• EMR application layer was static and could not
  adapt to workflow requirements
• Assumptions were not consistent with the diverse
  needs of a large dynamic organization
• Adding to this problem the entire project is
  the knowledge gap between the practitioners
  understanding of the EMR, and the vendors
  knowledge .

5
What is missing?

• Take an oval track

6
What is missing?

• Take an oval track
• Add cars

7
What is missing?

• Take an oval track
• Add cars
• Each car drives at constant speed of 20 mph

8
What is missing?

• Linear system analysis
• Each car will move around the track at a constant
  speed
• The location of any car can be predicted at any
  time due to constant velocity
• The only factor is the dynamics of the car
• Interactions of the car are ignored (allows for
  superposition)
• Non-linear system analysis
• http//www.youtube.com/watch?vSuugn-p5C1M
• http//www.youtube.com/watch?voEqakLERMbQ

9
What is missing?

• What they say Assumptions were not consistent
  with the diverse needs of a large dynamic
  organization
• What they mean Health care is a complex system
• What is a complex system http//www.youtube.com/w
  atch?vgdQgoNitl1g
• Complex systems Our intelligence, out
  consciousness, evolution, herd behavior, stock
  markets, and health care
• This was and is a breakthrough concept Order
  arising from chaos

10
From so simple a beginning.

• Boids
• Artificial birds developed to understand flocking
  behavior in nature
• Boids try to fly towards the centre of mass of
  neighboring boids.
• Boids try to keep a small distance away from
  other objects (including other boids).
• Boids try to match velocity with near boids.

11
Health care and complexity science

• Agents are independent but interconnected with
  other agents
• Agent reactions can have wide-ranging influence
  on the system
• Agents can be organizations, clinicians, or EMR
• Agents can have adaptive roles
• Self-organization
• 47 million without health care --- Why?
• 16 of US spending and soaring --- Why?

12
(No Transcript)
13
Health care and complexity science

• Nurses, medical interns, residents, and attending
  physicians begin their patient assignments
  (inherent order)
• Nurse realizes that an order for a medication is
  incorrect. The resident is informed and corrects
  mistake (co-evolution)
• The ED has three critical patients likely to be
  admitted to ICU and a planned surgical procedure
  in the ICU. A decision is made to cancel the
  elective surgery (adaptable elements)
• Medical error is often the result of breakdown on
  several levels
• There may be several causes all of which are
  necessary, but none of which alone are sufficient
  (emergent behavior)

14
Health care and complexity

• Implications of viewing health care through the
  lens of complexity
• Management
• Policy making
• Quality
• Human-center computing

15
Health care and complexity science
16
Complexity at the socio-technical level

• Implementing an EMR is perturbing an unstable
  system
• The current system has self-organized
• The EMR can act as an attractor spurring
  self-organization and have good or bad
  consequences
• This is not unlike the shock wave example
• How can complexity help?

17
Model of socio-technical systems

• Main source of input in socio-technical systems
  is money, people, motivation, training, and
  information
• The input acts as an attractor with the
  environment self organizing in response
• The output is a function of attractors and
  self-organization
• From a human-centered standpoint we must
  understand attractors and the self organization
  that will result
• This is hard.

18
Implementing EMR CAS Approach

• Task analysis How the work is supposed to be
  conducted
• Activity analysis How the work is conducted
• Communication
• Cooperation
• Decision making
• Interruptions
• Noise levels
• Collect data with RID tracking
• Collect data through ethnographic observation
• EMR
• What information is in current EMR or in paper
• How is current system being used
• How does it fit into the workflow

19
Implementing EMR CAS Approach

• Develop cognitive models for agents
• Identify human attributes which make the
  communication and coordination possible from
  activity analysis
• Model the environment
• Environment has objects and attributes
• Computer terminals, phones, etc
• Physical layout can have an effect on situational
  awareness
• Noise level
• Patients
• Varying acuity level
• Require various resources
• Arrive at varying frequency and acuity
  distributions

20
Implement EMR CAS Approach

• Develop multi-agent simulation
• Simulation will include current workflow
  patterns, agents, cognition for agents, current
  paper system, communication artifacts, etc
• Validate the system
• Once validated experimentation can begin
• Determine effect of EMR on environment
• Avoid pitfalls before implementation investment

21
THE END!
22
REFERENCES

• Pritchard., P. (2002). The self-organising
  system as a model for primary health care can
  local autonomy and centralisation co-exist?,
  Informatics in Primary Care.
• Dugdale, J. (2006). A Pragmatic Development of a
  Computer Simulation of an Emergency Call Centre.
  
• Chaffee, M. (2007). A Model of Nursing as a
  Complex Adaptive System, Nursing Outlook.

23
Other good sources