N. Blair Butterfield talks on: Macro Trends in Health IT - Past and Present

1. N. Blair Butterfield
Wind River Advisory Group LLC
www.windriveradvisorygroup.com
November 16, 2022
Macro Trends in Health IT
Past and Present

2. Quick Summary
๏ 30+ years in health IT
๏ Board Member, Spōk (NASDAQ) (9 yrs)
๏ Sr. Advisor, LEK Consulting (7 yrs)
๏ President, Philips VitalHealth (3 yrs)
๏ VP Int’l, GE eHealth Division (9 yrs)
๏ Exec. Team, Vital Images (7 yrs)
๏ Residing in Dubois, Wyoming
About me

3. Major health challenges
4 Common “Diseases”
๏ Rising healthcare costs (aging populations, chronic disease)
๏ Inefficiencies (scheduling, payment)
๏ Lack of access (too few doctors, especially in rural areas)
๏ Unsatisfactory quality (incomplete history, medication errors, etc)
4 Common “Treatments”
๏ Digitization of health information (HIS, EMR, PACS adoption)
๏ Longitudinal EHR (sharing data between existing systems)
๏ Chronic disease management (home health, devices, etc)
๏ Consumer empowerment (telemedicine, personal health records)
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4. The first systems
๏ The core system has always been the ADT (Admission,
Discharge, Transfer) system - the master
fi
le of patient
identity, contact information and other key data.
๏ The ADT system was a replacement for the paper
fi
les
used previously.
๏ First to adopt clinical solutions were hospital departments
- radiology and lab especially - starting commercially in
the 1970s and evolving rapidly.
๏ These were standalone systems (“islands of care”) that
did not sit on a network and could not share data (e.g.
PACS systems tethered to imaging machines).
๏ Other departments (e.g. blood bank, cardiology, etc)
came along and also adopted these standalone solutions.
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5. Organizational networks
๏ As intra-hospital networks (pre-internet) were established,
departmental systems were able to receive basic patient
information, principally from the ADT system.
๏ These departmental systems were optimized for
departmental work
fl
ows but did not share a common
architecture, user interface, or database.
๏ These came to be known as “best of breed” solutions.
๏ The end result was a hodge-podge of non-interfaced
systems that existed on a common network but had no
reliable way to interconnect.
๏ Similar to the early days of personal computers, where there
were separate and feature-rich applications for word
processing, spreadsheets, etc.
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6. Consolidation and competition
๏ As networks became more developed and the desire to share
information grew, tension arose between “best of breed” systems
and early enterprise systems.
๏ The tradeo
ff
- best of breed was highly optimized for the speci
fi
c
needs of a particular department.
๏ While the enterprise systems had a common database,
application architecture, and user interface (“look and feel”), and
could share data across its various modules.
๏ Hospitals landed on one side or the other of the fence, depending
on who called the shots in the procurement of health IT (CFO vs
department Chair vs CIO).
๏ Eventually the enterprise systems won out (like Microsoft O
ffi
ce).
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7. Government enters the picture
๏ In the early 2000’s, government decided to incentivize
the adoption of electronic health records for all citizens -
a huge goal that has not yet been realized despite a lot
of progress.
๏ Financial incentives (“Meaningful Use”) were introduced
to motivate providers to adopt Electronic Medical
Record (EMR) systems.
๏ The goals -
✓ to enable sharing of essential patient information
between providers (and with patients)
✓ to reduce the cost and improve the quality of care
(e.g. reduced test repetition, access to medication
histories, problem lists, etc.)
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8. Focus on data sharing
๏ It was envisioned by governments globally that a
comprehensive electronic patient health record (EHR) would
include data from multiple, independent providers.
๏ Thus the adoption of EMRs within organizations was
necessary but not su
ffi
cient to create an EHR and also meet
the mandates of Meaningful Use (MU).
๏ MU mandates required the sharing of a speci
fi
c set of
patient info with referring providers and with patients.
๏ But the standards for such data sharing were immature and
unreliable.
๏ Signi
fi
cant e
ff
ort was put into maturing standards, focused
on key work
fl
ows that supported the MU mandate - e
ff
orts
involving a major collaboration of standards organizations
and government policy leaders.
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9. The rise of HIT standards
Standards enable predictability
✓ Measurement, repeatability,
reproducibility
Standards enable specialization
✓ More vendors can “plug and play” in
the solution
Standards prevent “lock in”
✓ Not hostage to custom solutions
Standards reduce costs
✓ Lower install and TCO costs
Standards enable collaboration and lower costs
Key Standards

10. The arrival of health info exchanges
๏ As standards vied for supremacy, governments
around the world entered the race to establish a
national EHR.
๏ Health policies in many countries began to include
national ehealth initiatives, to network and share
patient health data across a region or entire country.
๏ Funding was appropriated to support these initiatives
and enable these goals - better care, at lower cost.
๏ National ehealth initiatives were laboratories - various
standards were tested, various architectures
adopted, various governance approaches used.
๏ In the end, regional networks (HIEs) gained the most
traction, especially those anchored by one (or more)
major provider organizations.
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11. Connecting more stakeholders
๏ In the USA, as MU eligibility was expanded,
the next step was connecting additional
stakeholders in the health IT ecosystem.
๏ This included patients, behavioral health, long
term care, pharmacies, etc.
๏ APIs an “app farms” were developed for
EMRs and other applications to enable
connectivity to be established.
๏ Additional standards (especially HL7 FHIR)
were
fi
nalized, to bring the whole ecosystem
into the same playing
fi
eld and allow data to
be shared.
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12. CDR
Automation
Basic CDSS
Guided Care
Collector
Documentor
Helper
2000
1980 1990
Colleague
The Path:
Enterprise EMR Generations
Enabling Advanced Healthcare Transformation
Mentor
2010 2020
Workflow
Support
✓ Shared baselines of best practice
✓ Advanced decision support
✓ Connected care management - home, physician office, hospital
✓ Intelligent surveillance & benchmarking – alert, guide, measure
Connectivity
Enhanced
care
Data
mining Connectivity
Present
Automation
The Goal:
Evidence-based Medicine
Future
Decision
support

13. Technology additions
๏ As more stakeholders are added, more modes of communication
have emerged as technology advances.
๏ Tools that can complement and enhance the core investment in
EMRs take precedence given the size of the investments and need to
optimize.
๏ The new frontiers include Arti
fi
cial Intelligence (AI), Natural Language
Processing (NLP), mobile, cloud native, Platform as a Service (PaaS),
Clinical Decision Support (CDS), Clinical Communication and
Collaboration (CC&C), and so on.
๏ The goals are still the same: higher quality care, at lower cost, now
often referred to as Value-Based Care (VBC), delivered through
Accountable Care Organizations (ACO’s), etc.
๏ The technologies, applications, and players will continue to evolve,
recombine, emerge, and disappear.
๏ But the goals will not change.
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14. Thank you for your attention.
Questions?
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