The Scope of Health IT: Progress and Challenges Summary

The Scope of Health Information Technology:
Progress and Challenges
Andrew Oram
This work is licensed under the
Creative Commons Attribution 4.0 International License.
September 9, 2015

Biographical information
The Scope of Health IT Andrew Oram
Andrew Oram is an editor at the technical publisher and information
provider O'Reilly Media, specializing currently in open source,
programming, and health IT. He is also a weekly correspondent on
health IT and health policy for HealthcareScene.com.
Email: andyo@oreilly.com
Twitter: @praxagora
Google+: https://plus.google.com/+AndyOram/
LinkedIn: http://www.linkedin.com/in/andyo/
SlideShare: http://www.slideshare.net/andyoram
There are no conflicts of interest in this presentation.

A vision of ideal health information technology
The intelligent hospital at HIMSS

Some innovations of the intelligent hospital
Medicines are checked against prescriptions via bar codes and delivered by
robots
EMTs radio the patient's status into the ER before the patient arrives
A buzzer goes off if a clinician approaches a patient without washing hands
first and a non-compliant event is recorded

Alternative visions of health IT
Things that IT does to us
Things that IT helps us do for ourselves
Sensors report whether an elderly person fails to get out of bed
A buzzer goes off if a clinician approaches a patient without washing hands
first
A black box records everything that goes on in the operating room
A secure texting system allows doctors to keep in touch with patients
A fitness device reports our progress during exercise
A personal health record allows us to maintain and share our data
(cf. Machines of Loving Grace by John Markoff)

The care universe and health IT
We start with an individual interested in his or her health
Individual

Add caregivers

Add health IT

What is a medical device?
We tend to think of a medical device
as this
But increasingly it's going to
look like this
or this

Devices: mobile
Useful for texting, delivering video, and other telehealth
Ubiquitous and usually affordable
Take blood glucose readings
But also can be used to:
Check athletes for concussions
Test vision
Measure footsteps for exercise and for fall prevention

Devices: roles
Monitoring: are you in bed or moving about?
Adherence: did patient take prescribed medication?
Delivery of alerts and information
Fitness, measuring vital signs, etc.

Devices: miniaturization and democracy
More computing power in the same space
Allows wide distribution to under-served populations and regions of the world
May allow untrained individuals to perform tasks formerly requiring expensive
technicians
Miniaturization and cost reductions driven by:
Cheaper, more accurate sensors
Lower power requirements
Simpler, more generic (commodity) materials

Devices: dependencies
Must access clinical data/analytics to deliver useful alerts and other information
Should interoperate with other devices and with EHRs, requiring:
An always-on network
Radios (cellular, WiFi, Bluetooth, Near Field Communication)
Standards
Data requires provenance (metadata about who generated the data, under
what circumstances, etc.) to be shared and stored in medical records

Devices: standards
Numerous Internet protocols covering data exchange
Yet each device manufacturer creates its own unique Application
Programming Interface (Apple's HealthKit may become de facto standard)
Fundamentals defined by IEEE in 11073 family
An alphabet soup of organizations is creating standards
Data Distribution Services (DDS) often recommended for interaction

IEEE 11073
Endorsed by the Continua Health Alliance
Multiple standards cover thermometers, pulse oximeters, blood pressure
monitors, etc.
Tries to create standard names, data structures
International participation in defining the standards

Data Distribution Services (DDS)
You can configure who is allowed to receive information, how often it is
transmitted, and other aspects of communication
Numerous filtering capabilities
A standard from the Object Management Group (OMG)
Uses the publisher/subscriber model (requires intermediate storage)
Very complex, trying to cover every eventuality (248-page specification)
Much more than I can mention here...

Mobile apps
Apps are extremely varied, and range from measuring footsteps to
collecting patient data for clinical assessment
Mobile apps rest heavily on:
Software apps in the fitness and medical spaces are growing robustly in
number and adoption
Comparing data from the user to results of clinical research
Linking users with other users of the app
Linking to other apps and data sources

Mobile apps: business models and regulations
Offering services, such as storage and advice
Collecting and often selling user data, which presents ethical issues
(security, consent) even if it is anonymized
Business models usually include:
Like devices, apps could benefit from standards
FDA has tried to keep a light touch, but still regulates apps that promise
diagnosis or treatment

Telehealth
Deliver reminders and encouragement over text messaging
Deliver information through video
Connect doctor to patient or consulting doctor remotely
Remote monitoring and the patient-centered medical home (PCMH)
Services such as WebMD, HealthTap,
and innumerable web sites
Patient portals
Adoption depends on changes to licensing rules and reimbursements

Telehealth: an illustration
Contact is the prerequisite for behavior change and coordinated care
More than 60% of Medicaid patients—often high-cost patients—disappear
from the view of states and health plans after leaving the hospital or clinic
A pilot shows:
Patient engagement rates jumped from 37% to 73%
ER visits per patient dropped from 7.14 to 3.45 per month
An app installed on the patients' devices helps them keep in touch

Telehealth: common uses
Delivering care to remote areas or those with little medical coverage
Continuous follow-up to care given in the doctor's office
For example, text reminders (which have demonstrated
positive effects on behavior) and informational videos
Detecting changes in health status in order to trigger urgent
intervention
Tracking behavior and health for research
Often, data can be collected by a less trained professional, or
even the patient herself, and transmitted to a health center
Innovate, low-cost devices contribute to telehealth

Add data exchange

Data exchange: HIE
Now learning to offer more added value, such as patient segmentation
Health information exchanges were historically expensive and could not
survive when initial grants expired
Still not widely adopted, because providers lack incentives to share data

Data exchange: roles
Clinical research
Analytics (big data)
Coordinated care
Marketing

Data exchange: standardizing
Data formats
Exchange protocols
Coding (ICD and SNOMED for diseases, codes for drugs, lab tests, etc.):
semantic interoperability
Trust

Data exchange: formats
FHIR: a modern programmer-friendly project including an open API
Blue Button: simple and somewhat more standardized
C-CDA: the current most widely used semi-standard
Apple, Microsoft, and other tech firms will try to establish their own standards
through projects such as HealthKit
SMART: An open standard now layered on top of FHIR

Fast Healthcare Interoperability Resources (FHIR)
RESTful API with the usual accoutrements:
Multiple common formats (XML, JSON)
Goal: bring health data exchange into the 21st
century
Must be implemented in stages because of its size and the variety of health
care settings
OpenID and OAuth for authentication
Needs to be supplemented with “profiles” for specific medical disciplines and
other situations

SMART
API allows data to be extracted (not inserted) into EHRs
Developed at Harvard Medical School with government (ONC) support
Good complement to FHIR (a more recent standard), for which SMART fills
in some of the gaps, such as profiles
Reference implementation and sample apps developed

Data exchange: protocols
CONNECT
Application Programming Interfaces: FHIR, SMART
Direct
Blue Button+ (works though Direct)
All forms of data exchange assume a working network with sufficient
bandwidth

Data exchange: trust
Direct Trust project depends on certificates, somewhat like Web browsers
How do you know that the person asking for a record is genuine?
One role of health information exchanges is to authenticate correspondents
We await corresponding mechanisms for FHIR and SMART

Images
DICOM does not cover the radiologist's annotations that make an image
useful, and therefore they follow no standard
Images are officially standardized through DICOM
Size of images drive clinics and hospitals to use cloud storage

Genetic testing and other “omics”
Popular self-testing has limited value
Low cost of genome sequencing allow more and more patients to benefit
from targeted treatments
Size (3.2 gigabytes per human genome) places strains on storage and data
transfer for research
Genomic data is considered to be impossible to deidentify
“Cells are just living, squishy parallel processors”—synthetic biology developer

Add other actors
(For an even more overwhelming view of sharing patient data, see The Data Map)

Analytics: roles
Patient segmentation or risk stratification (who is most at risk of hospitalization?)
Utilization of resources (rooms, staff, equipment)
Cost setting (capitation)
Research (new treatments, clinical prediction models)
Patient peer data sharing (such as PatientsLikeMe and the
Personal Genome Project)
Physician comparison (is one doctor doing way better than others?)

Analytics and randomized double-blind trials
Contrasts
Much debate over the relationship
Clinical trials use samples that are not representative of the general
population, whereas “big data” takes known facts about this population
Complementarity
Clinical trials have strictly controlled variables, whereas “big data”
analytics deal with messy data
Use analytics during clinical trials to influence its direction
Use analytics on population health data to find promising hypotheses
for clinical trials
Use analytics after clinical trials to check results, such as adverse effects

Why analytics are central to change
Clinical trials can be augmented through data collected on ordinary people
Accountable care requires data about how well interventions are working and
how resources are being used
Behavior change requires predictions about expected outcomes
But analytics also have a dependency: they are far more accurate with large
data sets, which requires interoperable records
Consumer choice requires transparency: accurate data on provider prices and
quality
Unexpected insights such as adverse drug effects require big data analysis

Electronic health records (EHRs)
APIs allow innovation by third-party developers — all-in-one solutions are not
sustainable
Security is crucial (and broadly missing) but frustrating to users
Open source software would allow both interoperability and limitless innovation
Interoperability supports care coordination and research, but still elusive
Usability is critical to safety, and can be determined only during realistic use
with input from clinicians

Informative: Conveys necessary information between staff, along with
relevant alerts and other useful information
Some aspects of quality in electronic
health records (page 1)
Complete: Allows you to do what you need, and combines all inputs, such as
lab reports and pharmacies
Correct: Does what you ask
Orderly: Fits natural workflows, organizes information in the way the clinician
needs, and enforces protocols
Clear: Simple and easy to use
(Usability is an organic, interactive quality, so lists of traits like this may
be of limited value.)

Secure: Enforces appropriate access by individuals or teams
Some aspects of quality in electronic
health records (page 2)
Flexible: Users can customize the system to match their behavior
Consistent: Acts predictably no matter what part of the system you're in or
who you are
Fast: Responds in time to preserve the user's train of thought (voice
recognition is popular), does not interrupt with needless alerts
Intelligent: Draws inferences, eliminates duplication of user effort
Failsafe: Preserves intentions of the user, is not confusing, and allows
easy error correction

Security—regulation
Regulations vary state by state and are intimidating
HIPAA does not prevent doctors from sharing data with patients—in
fact, it requires patient access

Two-factor authentication (e.g., a password and card or fingerprint) is
required in some cases (e.g., prescription of controlled substances)
Security—technology
Encrypt data “in transit” (over email or the Web)
Encrypt data “at rest” (such as in an electronic health record system)
Prohibit removal of data, or require encryption on laptops and other devices
Enforce strong passwords
Fundamentals are the same as in any computer system
Assign rights to teams or individuals on a precise, as-needed basis
Log all accesses and issue alerts on suspicious accesses

Accuracy: Errors more likely to be fixed
Personal health records (PHRs)
Can store any data entered by the individual, including device output and other
patient-generated data
Stored by the patient, not the clinic or hospital
Advantages of giving patients control over their records:
Data exchange: patients can share with clinicians, caregivers, and
researchers
Privacy: no hidden data transfers without patient consent
Patient maintains full control over who gets the data

What makes an individual adopt healthier habits?
Motivations to change
Individuals (patients/consumers)

Motivations to change: individuals
Individuals' goals differ from their clinicians' goals
Individuals want to know what precise steps will benefit them
Both peer and clinical relationships are central
Sustained support from clinicians is needed to drive recalcitrant patients to
change
Encouragement works better than exhortation (accentuate the positive)
Health IT can keep the team in contact and provide consistent contact with the
individual trying to improve

Individuals require continuous support from clinicians and/or peers

Motivations to change: clinicians
Vague mandates (i.e., concern for quality) cannot be consistently honored
Clinicians are busy, often undercompensated, and sensitive to rewards and
penalties
Payers must establish frameworks for coordinated, long-term care in order to
set positive priorities for clinicians

Clinicians' priorities are driven by payers

Individuals' priorities are also influenced by payers

Motivations to change: payers
Many payers would like to reward outcomes (fee for value)
Insurers are getting worried that individuals or their employers will drop or cut
back on coverage because costs are outrageous
To reward outcomes, the cost of a patient or group of patients must be
accurately set
Fee for value depends on research and the analysis of huge collections of data
on patient diagnoses and outcomes

Payers need research and analytics to set payments

Motivations to change: researchers
Research without short-term pay-offs depends on government funding
Research and analytics are labor-intensive fields with high costs
The government is also responsible for setting the agenda for public health
research

Researchers' funding and agendas are controlled by government

Government also makes payments and regulates payers

Motivations to change: government
Powerful pressure from an organized and aware public can overcome inertia
Disruptive change by law and regulation is thwarted by incumbent institutions
that:
External pressures such as rising costs can encourage change, but public
activism is required to turn change in a constructive direction
Possess ample funds for lobbying and publicity
Employ a large staff (read: self-interested voters)
Warn of failure and fulfill their own predictions

Governments respond to pressure from the public

Pay for performance rests on accurate data about costs, requiring
analytics
Dependencies—technical
Analytics are severely limited without data exchange among EHRs
Devices require networks and standards to share data
Data sharing requires provenance

Dependencies—organizational
Most individuals won't make major lifestyle changes without sustained
support from clinicians (coordinated care) and/or peers
EHR vendors will not implement data exchange until required by purchasers,
who currently are hospital and clinic administrators
Clinicians will instituted coordinated care when required by payers (pay for
performance) or by competition from disruptive sectors

Use insights from research
Roles for clinicians in improving health IT
Insist that procedures and records be focused on clinical care
Participate in design of record systems
Send data to patient and to next stage of care
Use patient-generated data
Adopt telehealth where legal and feasible

Roles for developers in improving health IT
Start with a business model that respects where the power and money lie in
the health care industry, but try to disrupt it
Bring the clinicians or other users onto the development team
Adhere to standards but look for the purpose that lies behind them and beyond
them
Fiercely protect patient privacy—earn their trust

Some topics not mentioned in this talk
Gamification (.e.g., brain strengthening apps)
Artificial intelligence support, or evidence-based medicine on steroids (e.g.,
IBM's Watson)
Effects of technology on clinician/patient interaction

Some U.S. organizations in health IT
American Health Information Management Association (AHIMA)
Healthcare Information and Management Systems Society (HIMSS)
Food and Drug Administration (FDA)
Office of the National Coordinator (ONC)
Center for Connected Health (CCH)
Patient Privacy Rights (PPR)

Some health IT resources
Trotter, F., & Uhlman, D. Hacking Healthcare. O'Reilly Media, October 2011
President’s Council of Advisors on Science and Technology. PCAST report —
Realizing the Full Potential of Health Information Technology to Improve
Healthcare for Americans: The Path Forward. December 2010.
Commonwealth Fund. A Vision for Using Digital Health Technologies to
Empower Consumers and Transform the U.S. Health Care System. October
2014.
Oram, A. The Information Technology Fix for Health:
Barriers and Pathways to the Use of Information Technology for Better Health
Care. O'Reilly Media, April 2014.

Contact
Email: andyo@oreilly.com
Twitter: @praxagora
Google+: https://plus.google.com/+AndyOram/
LinkedIn: http://www.linkedin.com/in/andyo/
SlideShare: http://www.slideshare.net/andyoram

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