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Healthcare leaders need to motivate change and constantly advocate for innovation, particularly in the
IT ecosystem. I personally approach this from a position of humility, fully understanding that I have not
mastered what I am advocating. I’m not satisfied as a patient or as a citizen with the current trajectory
of digital health at the macro level. Nor is Health Catalyst satisfied with where it is on this trajectory. We
are far from perfect, with plenty of flaws, which is why we are intentionally disrupting ourselves with the
idea of the Health Catalyst® Data Operating System (DOS). I will offer some criticisms about healthcare
IT, with the understanding that it is from the position of wanting to do better, while being personally
and organizationally accountable.
The idea of DOS is equally important for C-Suite executives as it is for IT-domain leaders. Software
runs everything today, and executives need to understand these technical topics because the most
expensive capital purchase won’t be a hospital, but an EHR. Recent ransomware attacks on health
systems are a case in point. If leadership can’t own up to the notion that software now runs the
company, for better or worse, it is behind the times. Healthcare CEOs who will thrive going forward will
understand their software technology and data. They will be the leaders who rise to the top in the next
generation of U.S. healthcare.
By Dale Sanders President of Technology Health Catalyst
Seven Ways DOS™ Simplifies the Complexities of Healthcare IT
Executive Report
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The Components of DOS
What is the Health Catalyst DOS and why is it different from traditional data warehousing? Is it real or
just a buzz phrase? Can it be implemented? If so, what are the implementation options? And why does
healthcare need one now more than ever?
As Figure 1 illustrates, DOS combines real-time, granular data and domain-specific (e.g., healthcare),
reusable analytic and computational logic about that data, into a single computing ecosystem for
developing applications. DOS can support the real-time processing and movement of data from point to
point, as well as batch-oriented loading and computational analytic processing on that data. This amounts
to the merging of a data warehouse and an HIE.
Figure 1: The Health Catalyst Data Operating System
This DOS involves three layers, each supported by multiple components:
Layer #1: Data Platform
Catalyst Analytics Platform – Batch-oriented, less-than-real-time analytics calculations
and computational services.
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Core Data Services – Pattern recognition, Natural Language Processing (NLP) governance tools,
metadata repositories, and data quality tools across subject areas.
Real-time Data Services – Real-time data streaming and processing, a reference Lambda Architecture
(the ability to process real-time and batch-oriented data for analytics in the same ecosystem), and HL7.
An emerging improvement on Lambda is Kappa Architecture, the combination of real-time and analytics
processing, and batch-oriented processing in the same environment.
Layer #2: Fabric and Machine Learning
The fabric is the layer of clinical and business logic and services laid on top of the granular data and
services. This layer includes open application program interfaces (APIs), with an emphasis on developing
FHIR-based services, where possible. Where it’s not published or possible, Health Catalyst will extend
FHIR or pursue other means, but FHIR will be the default for services in the fabric layer.
Also, a native part of the fabric is the notion of machine learning, which is embedded as part of virtually
everything Health Catalyst does.
Layer #3: Applications
The top layer of DOS consists of applications built by Health Catalyst, hospital and clinic IT development
teams, and third parties.
These layers comprise the high-level DOS architecture, but a key point is that Health Catalyst is
developing the fabric to lay over a variety of topologies and data platforms, including platforms other than
what Health Catalyst produces. The fabric will be compatible with, for example, IBM, Oracle, Epic, Cerner,
and homegrown data warehouses. A Health Catalyst granular data platform is only one option and an
important part of the future.
There are three big differences between DOS and a traditional data warehouse:
1. Real-time transaction data and analytic computations in a single ecosystem that supports
everything.
2. A fabric of microservices and data bindings that can lay on top of any data system, not just the
Health Catalyst platform.
3. System design that uses open APIs, making the data platform and fabric services available to
third-party application developers and builds for future extensibility.
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Seven Attributes of DOS
The healthcare DOS is defined by seven attributes:
1. Reusable clinical and business logic – Registries, value sets, and other data logic lays on top
of the raw data to be accessed, reused, and updated through open APIs in the healthcare IT
environment, specifically enabling third-party application development against it.
2. Streaming data – Near- or real-time data streaming from the source all the way through to the
expression of that data through DOS, that can support transaction-level exchange of data or
analytics processing.
3. Integrated structured and unstructured (text) data in the same environment – This will
eventually incorporate images.
4. Closed loop capability – Methods for expressing knowledge in DOS, including the ability to
deliver that knowledge at the point of decision making (e.g., back into the workflow of source
systems, such as an EHR.)
5. Microservices architecture – The ability to update constantly, with continuous development and
release. This eliminates the painful upgrades to which healthcare has become accustomed and
desensitized. In addition to abstracted data logic, open microservices APIs exist for DOS
operations, such as authorization, identity management, data pipeline management, and DevOps
telemetry. These microservices also enable third parties to develop applications on DOS without
having to recreate them.
6. Machine learning – DOS natively runs machine learning models and enables rapid development
and utilization of those models, embedded in all applications. This is a primary strength of the big
data Hadoop ecosystem that came out of Silicon Valley. It is natively designed to support machine
learning and computational analytics that traditional relational databases cannot.
7. Agnostic data lake – Some or all of DOS can be deployed over the top of any healthcare data
lake. The reusable forms of logic must support different computation engines (e.g., SQL, Spark
SQL, SQL on Hadoop).
These are the required attributes for becoming a healthcare DOS and meeting the future needs of data in this industry.
Why DOS Is Important in Healthcare Right Now
A convergence of things happening around the country (Figure 2) is driving the business need for DOS.
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Figure 2: What’s driving the need for DOS in healthcare
New big data technology has emerged subsequent to open source collaboration in Silicon Valley. We are
fortunate to be here at this point in history to take advantage of what Facebook, Google, Amazon, Twitter,
and others have developed for the public to consume free of charge. The value we can derive from these
services is significant.
Healthcare expenses continue to rise and are anticipated to hit 20 percent of GDP by 2025. This is
cannibalizing the U.S. economy, and if healthcare cannot change the trajectory through digitization, it
spells trouble. We are eating up the future of the country in healthcare expenses.
Physicians are burned out in large part because of the technology they are now using. It’s taking time
away from clinical care and human decision making. More than 50 percent of their time is spent in front
of a computer instead of a patient. This has to change. Personal health records (PHRs) have been
unsuccessful on several fronts, interoperability being one. It’s also in no one’s economic interest to
surrender data to a PHR that’s transportable from one facility to another. Until PHRs are successful,
patients can never really be at the center of care.
FHIR is emerging and we should be very optimistic about this. There have been concerns with HL7 and
its message-oriented architecture in the past, but credit is due to the rebels within HL7 who started FHIR.
It’s a very well-founded framework.
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HIEs have largely been unproductive. When arguing from the position of data or logic, HIEs have been
unsuccessful on many levels. Economic models and technical usability of the data within the EHR have
not worked, and it’s time to do something different.
The good news is that EHRs have been deployed like never before, so the digitization process is starting.
Data is available now that wasn’t in the past. The problem is that those EHRs are still not well received.
54 percent of doctors surveyed in 2016 said the EHR has detracted from efficiency, and only 25 percent
said it has improved efficiency. There is a lot of opportunity for working with doctors to improve EHRs.
Content Is King, the Network Is Kong
When looking at modern businesses, data content is becoming the driving force behind business
strategy and value. Companies like GE, Tesla, Google, Facebook, Amazon, United Healthcare, and
Optum, all understand the value of data content and are pursuing it. But the network around that data is
as important as the data content itself.
Consider Metcalfe’s Law—the value of a telecommunications network is proportional to the square of the
number of connected users of the system—to understand the value of the community around data, versus
the hub and spoke model. Sticky relationships occur with great data content and a network of people
around that content. The reason Google Plus never took off (and yet Facebook is still accelerating) is the
combined content and network of people who make that sticky relationship with Facebook difficult or
impossible to transport to Google Plus. The executive of the future must understand the need for data
content and a network of people—patients, healthcare providers, physicians, and researchers—around
that data. This will create the sticky relationships successful businesses need going forward.
The Healthcare Digitization Index
The McKinsey Global Institute produces a Healthcare Digitization Index (Figure 3) that is a product of
data assets, data usage, and skilled labor. Essentially, this translates to what kind of data an industry
has, how the industry is using it, and whether the industry has the skilled labor to take advantage of that
data. Healthcare is one of the least digitized sectors among large U.S. industries. The graph in Figure 3
plots this low digitization score against the y-axis of three-year changes in post-tax profit margin to show
that healthcare is extremely anemic.
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Figure 3: The Healthcare Digitization Index
There’s a strong correlation between digitization index and post-tax profit margin. As margins get tighter
to manage, executives need to understand the importance of digitization to retain whatever competitive
edge they might still have.
C-Level Advice for a Digital Healthcare Future
Population health, value-based care, and precision medicine are data centric, so executives need
a strategic data acquisition strategy that goes beyond bricks and mortar. It’s imperative to think about
the data needed for managing population health, risk contracting, and precision medicine, and how it
will be acquired.
Healthcare organizations need a chief analytics or chief data officer. This is critically important. And will
this be the CIO or a new position? Regardless, someone must be appointed to fill the role to manage,
and be the executive cheerleader for, this critical asset.
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Physicians and nurses are over measured and undervalued, and this is in large part because they are
controlled by data entry and poor software. C-Suites should push all vendors to follow modern, open
software APIs, including, but not limited to, FHIR. This cannot be relegated to others, which would
minimize its importance in the organization. C-Suites need to be aware of the impact software has on the
business and capabilities of these open APIs. DOS concept is necessary and can be created by
leveraging and expanding the capability of the enterprise data warehouse, if one exists.
How DOS Addresses Healthcare System Needs
DOS addresses seven substantial U.S. healthcare system needs:
#1. Complexities of Healthcare Data Management & Acquisition
The first need starts with a shark tank story from a business perspective. I was in the audience of
healthcare IT startups pitching great software applications and creative ideas about healthcare. As
brilliant as they were, none offered a solution for the underlying healthcare data they needed. All had
decent demo data, but no answer for the massive acquisition of data and the scalability of that acquisition
across an entire industry. Nor did they have an answer for both clinical and business logic that resided
on top of that data. Startups like this, with great ideas and applications, need data. They cannot possibly
afford to build the data infrastructure and skills Health Catalyst offers. Nor can the industry afford it. It is
not scalable.
Computer science has greatly expanded modern programming languages at the top of its ecosystem
(Figure 4). There are many things we can now build quickly with different libraries and awesome
programming environments. This goes beyond the languages, to the DevOps tools that support the
languages, giving us the ability to manage and measure applications once they’re in the field.
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Figure 4: The data content layer needs to be updated in the computer science ecosystem
Modern databases and modern movement of technology exists thanks to Hadoop and a big data Apache
ecosystem. This all sits on top of modern operating systems, like iOS, Android, Windows, and Linux.
But application development still needs a solution for the middle layer, the raw data content that’s bound
and organized according to the domain it needs to support. Great programmers take advantage of great
languages (at the top of the ecosystem) and technology (at the bottom), but it’s still painful for them to
recreate the data content and organized logic around that content that exists in healthcare. This would
require building a dozen or more Health Catalyst-type organizations in the industry. It’s unscalable.
#2. Integrating Data in Mergers and Acquisitions
A new company isn’t integrated until the data is integrated. Executives jump into mergers and acquisitions
and, within a few months, realize they can’t pull together basic financial reports about the new company,
much less complicated clinical quality measures that put their reimbursement at risk. HIEs are not
sufficient for this kind of data integration. They barely support rudimentary clinical integrations, much less
balancing a new company’s general ledger. Ripping and replacing EHRs with a single common vendor
is not an affordable strategy for interoperability. Besides, hybrid vigor is a good thing in this context. It’s
not a good idea, long term, to put all the organization’s digital and data eggs in one vendor’s platform.
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Rip and replace is not an answer for mergers and acquisitions (M&A). Keep the existing, disparate source
systems, like finance, supply chain, registration, scheduling, A/R, and EHRs. These are just a few of
many source systems to deal with in an M&A. But they can all be virtually integrated with DOS, and
transaction-level data can be shared the same as with an HIE. DOS can integrate data for common
metrics around finance, clinical quality, and utilization, without replacing those source systems.
#3. Enabling a Personal Health Record
Healthcare needs to finally enable a PHR. A patient could have multiple records depending on how
many places she has lived and her various care environments. It’s up to her to figure out how to
consolidate all that data into a concise PHR that she can move around and share as she feels
appropriate. This is not putting the patient at the center of healthcare.
With DOS, a fabric can lay over all these disparate systems and pull them together. Healthcare should
think about pulling these systems into a single, effective PHR, Microsoft’s HealthVault. Regardless, we
can provide a better PHR than current offerings. And patients are not willing to manually enter and
consolidate all their personal health data into one repository. They don’t have time. That content has to
be seeded by the data that exists in the different facilities and treatment areas.
#4. Scaling Existing, Homegrown Data Warehouses
Homegrown data warehouses are easy to start and build, but expensive to evolve and maintain. There
are a lot of them in healthcare and there is no easy way to retire them. Ripping and replacing with another
vendor solution isn’t an option, as mentioned earlier.
This is what motivated Health Catalyst to develop DOS. We can lay Health Catalyst (and other)
applications over DOS fabric, which can then be laid over the top of homegrown data warehouses. We
should expand this market because the value to the industry isn’t necessarily in the aggregation of
granular data (this is quickly becoming a commodity). The value is in the logic that resides on top of the
fabric and applications, and the models that reside on top of the granular data.
#5. The Human Health Data Ecosystem
When I was working on the Alberta Health Services population health initiative, we concluded that only
eight percent of the data needed for precision medicine and population health resides in today’s EHRs.
Even less data is available for healthy patients. Figure 5 shows the datasets that come from other
sources. The ability to have a scalable platform for ingesting data and a scalable fabric on top of that is
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only going to get more challenging if this is not addressed. We will never achieve precision medicine and
population health without something like DOS.
Figure 5: Only eight percent of data needed for precision medicine and population health resides in EHRs
Ingesting healthcare data is a commodity
Ingesting healthcare data into a data lake or data warehouse is now essentially a commodity, thanks to
open source technology and a late-binding, schema-on-read approach to data models. It’s fast and cheap
to ingest data, but understanding the data content, data models, and vastly complicated nuances of
healthcare data will not be commoditized in our lifetime. The analytic logic or data bindings that apply to
that data, how to organize it into meaningful chunks, and the technology and skills to deliver to the right
person at the right time, in the right modality, all contribute to this complexity.
The mundane process of keeping up with changes in the source system data—change data capture—is
enormously complicated. Data quality management and scaling all this for a single healthcare system is
not going to become a commodity—ingesting data is.
Data content and sources
The volume of data content and sources in the Health Catalyst library illustrates how impossible it would
be for the industry to scale them if left up to individuals to do on their own. Health Catalyst has a long list
of different data source systems, which is just the beginning of the healthcare data ecosystem. With every
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data source, we understand data models, and how to access, bind, and use the data across
the continuum of care.
1. Affinity – ADT/Registration
2. Allscripts – Ambulatory EMR Clinicals
3. Allscripts Enterprise/Touchworks – Ambulatory EMR
4. Allscripts Sunrise – Acute EMR Clinicals
5. Aprima ERM
6. Cerner – Acute EMR Clinicals
7. Cerner – PowerWorks Ambulatory EMR
8. Cerner HomeWorks – Other
9. CPSI – Acute EMR Clinicals
10. eClinicalWorks – Ambulatory EMR Clinicals
11. Epic – Acute EMR Clinicals
12. Epic – Ambulatory EMR Clinicals
13. GE (IDX) Centricity – Ambulatory EMR Clinicals
14. McKesson Horizon – Acute EMR Clinicals
15. McKesson Horizon Enterprise Visibility
16. Meditech 5.66 EHR w/DR
17. NextGen – Ambulatory Practice Management
18. Quality Systems (Next Gen) – Ambulatory EMR Clinicals
19. Siemens Sorian Clinicals – Inpatient EMR
HR/ERP data sources
1. API Healthcare – Time and Attendance
2. iCIMS
3. Kronos – HR
4. Kronos – Time and Attendance
5. Lawson – HR
6. Lawson – Payroll
7. Lawson – Time and Attendance
8. Maestro
9. MD People
10. Now Solutions Empath – HR
11. Oracle (PeopleSoft) – HR
12. PeopleStrategy/Genesys – HR
13. PeopleStrategy/Genesys – Payroll
14. Ultimate Software Ultipro – HR
15. WorkDay
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EMR data sources Claims data sources
1. Allscripts – Case Management
2. Apollo – Lumed X Surgical System
3. Aspire – Cardiovascular Registry
4. Carestream – Other
5. Cerner – Laboratory
6. eClinicalWorks – Mountain Kidney DataExtracts
7. GE (IDX) Centricity Muse – Cardiology
HIE data sources
1. Adirondack ACO Clinical Data fromHIXNY (HIE)
2. ADT HIE Patient Programs
3. Vermont HIE
Clinical specialty data sources
1. 835 – Denials
2. Adirondack ACO Medicare
3. Aetna – Claims
4. Anthem – Claims
5. Aon Hewitt – Claims
6. BCBS Illinois
7. BCBS Vermont
8. Children's Community Health Plan (CCHP) – Payer
9. Cigna – Claims
10. CIT Custom – Claims
11. Cone Health Employee Plan (United Medicare) – Claims
12. Discharge Abstract Data (DAD)
13. Hawaii Medical Service Association (HMSA) – Claims
14. HealthNet – Claims
15. Healthscope
16. Humana (PPO) – Claims
17. Humana MA – Claims
18. Kentucky Hospital Association (KHA) – Claims
19. Medicaid – Claims
20. Medicaid – Claims – CCO
21. Merit Cigna – Claims
22. Merit SelectHealth – Claims
23. MSSP (CMS) – Claims
24. NextGen (CMS) – Claims
25. Ohio Hospital Association (OHA) – Claims
26. ProHealth – Claims
27. PWHP Custom – Claims
28. QXNT – Claims
29. UMR Claims Source
30. Wisconsin Health Information Organization (WHIO) – Claims
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Clinical specialty data sources 8. HST Pathways – Other
9. ImageTrend
10. ImmTrac
11. Lancet Trauma Registry
12. MacLab (CathLab)
13. MIDAS – Infection Surveillance
14. MIDAS – Other
15. MIDAS – Risk Management
16. Navitus – Pharmacy
17. NHSN
18. NSQIPFlatFile
19. OBIX – Perinatal
20. OnCore CTMS
21. Orchard Software Harvest – Pathology
22. PACSHealth – Radiology
23. Pharmacy Benefits Manager
24. PICIS (OPTUM) Perioperative Suite
25. Provation
26. Quadramed Patient Acuity Classification System – Other
27. QXNT/Vital – Member
28. RLSolutions
29. SafeTrace
30. Siemens RIS – Radiology
31. SIS Surgical Services
32. StatusScope – Clinical Decisions
33. Sunquest – Laboratory
34. Sunrise Clinical Manager
35. Surgical Information System
36. TheraDoc
37. TransChart – Other
38. Varian Aria – Oncology
39. Vigilanz – Infection Control
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Patient Satisfaction data sources
1. Fazzi – Patient Satisfaction
2. HealthStream – Patient Satisfaction
3. NRC Picker – Patient Satisfaction
4. PRC – Patient Satisfaction
5. Press Ganey – Patient Satisfaction
6. Sullivan Luallin – Patient Satisfaction
1. AHRQ Clinical Classification Software (CCS)
2. Charlson Deyo and Elixhauser Comorbidity
3. Clinical Improvement Grouper (Care ProcessHierarchy)
4. CMS Hierarchical Condition Category
5. CMS Place Of Service
6. LOINC
7. National Drug Codes (NDC)
Other healthcare data sources
1. 2010 US Census Detail for State of Colorado
2. Affiliate Provider Database
3. All Payer All Claims (certain States) ---In process UT, CO, MA
4. Alliance Decision Support
5. Allscripts – Ambulatory Practice Management
6. Allscripts – Patient Flow
7. Allscripts EHRQIS – Quality
8. Avaya
9. Axis (MDX)
10. Bed Ready – Other
11. Cerner Signature
12. CMS Standard Analytical Files
13. Daptiv
14. Echo Credentialing – Provider Management
15. ePIMS
16. First Click-Wellness
17. FlightLink
18. GE (IDX) Centricity – Practice Management
19. HCUP (NRD, NIS, NED Sample sets)
20. Health Trac
21. HealtheIntent
22. Hyperion
23. InitiateEMPI
24. Innotas
25. IVR Outreach Detail
26. MIDAS – Credentialing Module
27. Morrisey Medical Staff Office for Web (MSOW)
28. National Ambulatory Care Reporting System (NACRS)
29. Nextgate EMPI
30. Onbase
31. PHC Legacy EDW
32. QXNT/Cactus – Provider
33. SMS Legacy – Other
34. Truven Quality
35. University HealthSystem Consortium – Clinical and Operational Resource Database
36. University HealthSystem Consortium – Regulatory
34. Truven Quality
Master reference and terminology data content
8. National Drug Codes (NDC)
9. NPI Registry
10. Provider Taxonomy
11. Rx Norm
12. CMS/NQF Value Set Authority Center
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This is all the data we have in the U.S. healthcare ecosystem today and we have barely started. Imagine
what the future data ecosystem looks like. We must create a more scalable way for ingesting that data,
organizing it, and delivering it back to the point of decision making. What we offer with traditional data
warehousing and with what’s emerging from the EHRs will not scale to this volume and variety of data
sources
#6. Providers Becoming Payers
The insurance industry is the tail wagging the healthcare dog. The current payer insurance economic
model isn’t working. To improve the situation, providers need to model an assumed financial risk and
compete with, or completely disintermediate, insurance companies. With DOS, providers have more and
better data to model and manage risk than insurers. This is the hybrid we need in the future: providers
becoming payers to change the situation that’s so unhealthy for the industry.
#7. Extend the Life and Current Value of EHR Investments
DOS can extend the life and value of current EHR investments. Initially, the expectations of EHRs were
high. We haven’t quite reached the trough of those expectations yet (Figure 6), but as we start to optimize
EHRs and try to make them work in different revenue cycles, with population health and different
reimbursement models, reality will eventually settle in.
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Figure 6: The expectation of EHRs over time
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With open APIs and DOS, we can reduce this trough's depth and increase (and achieve)
the expectations that we set for EHRs a while back. EHR vendors need to participate in
the development of DOS and open APIs to make their products better.
Dr. Robert Pearl, CEO of the Permanente Medical Group, said that healthcare
is using “information technology from the last century.” This is a big statement from an executive
who leads 9,000 physicians and 34,000 staffers at one of the more impressive healthcare systems
in the world.
The inevitable technology lifecycle impacts the demand for EHRs (Figure 7). We’ve invested more
than $36 billion dollars on EHR Incentive Program payments. Federal incentives
artificially stretched demand, but that has passed. The underlying software and database
technologies of EHRs were commoditized long ago. Following the demand curve in the near future
portends trouble for EHRs.
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Figure 7: Lifecycle vs. demand for technology products
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Nobody has the appetite to replace all the outdated, last-century technology, but with DOS and open
APIs we can change the trajectory. This pivot toward extending and reinventing products needs to start
while in the comfort zone of the maturity phase. This is where Health Catalyst is now. We don’t want to
wait to be disrupted by someone else, so we’re going to disrupt ourselves. We can improve this curve
for the EHR vendors to the betterment of the industry.
Collaboration Role Models
Vendor collaboration from Facebook, Google, Amazon, Microsoft, and Twitter is a role model for
healthcare. The evidence is very clear that healthcare has a long way to go toward achieving this kind
of partnership. Some EHR vendor app stores appear to support open APIs, like FHIR, but by contract,
any application submitted allows the vendor to take the intellectual property and profit from it. We
need to collaborate on standardization and compete on innovation, which is exactly what the vendors
in Silicon Valley are doing. They know that, at the end of the day, innovation, not mundane standards,
achieves significant advancements.
The Rapid Pace of Change
The list of relevant open-source technology products available from Silicon Valley (Figure 8) changes
literally every week. This is an example of what can be done when the focus is on collaboration,
followed by innovation around that collaboration and standardization.
Figure 8: The fleeting matrix of open-source technology products
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The same thing applies to software development tools. We are at the beginning of a software
technology renaissance and healthcare has to take advantage of it. We must put pressure on ourselves
to do better.
The Possibilities of Open, Standard Software APIs
“EHRs would become commodity components in a larger platform that would include other transactional systems and data warehouses running myriad apps, and apps could have
access to diverse sources of shared data beyond a single health system’s records.” This statement
about open, standard APIs may sound threatening to EHR vendors, but only if they don’t participate in what’s happening. They can leverage the concepts in these open APIs to be more
competitive and extend their product lifecycle and value.
We can leverage open APIs technologically now more than ever before. I have a deep history of open-
system standards evolution. I know the major patterns of success and failure that started back in 1983
with my first exposure to Abstract Syntax Notation One (ASN.1). Now, success can be characterized by
things like FHIR and JavaScript Object Notation (JSON), two standards that are indicative of the current
renaissance.
When my team built the data warehouse at Northwestern Memorial Healthcare, we didn't call it DOS, but we had what amounted to an early version of it in 2006 (figure 9).
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Figure 9: A prelude to DOS
This is to prove why DOS is a very tangible concept. We now have the tools, techniques, and more data
content than we’ve ever had before, enabling us to build it like never before. In Northwestern’s data
warehouse, we supported analytics and near real-time exchanges of single records, and we were pushing
data point-to-point before there were HIEs. We had text data and discrete data in a single platform. We
ran analytics and batch processing computations on that data. We could also pull up just single records
and display those in an application (e.g., single lab results served up through the data warehouse). This
was all running on an early version of a Microsoft SQL server, which is much better now, with the ability
to handle mixed environments. Add the big data technology coming out of Silicon Valley and this concept
is easily achievable. This DOS is not just a pipe dream. We’re going to do this and it’s not that far away.
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Hybrid Big Data-SQL Architecture
In a paper titled “The Data Warehouse DBMS Market's 'Big' Shift,” Gartner analysts Mark Beyer and
Roxane Edjlali wrote, “Because traditional data warehouse practices will be outdated by the end of 2018,
data warehouse solution architects must evolve toward a broader data management solution for
analytics.”
This is why we are disrupting ourselves now. We have the ability to pull data in and take advantage of
streaming pipelines through tools like Kafka and Spark. (Figure 10).
Figure 10: The Hadoop, big data ecosystem provides options that we never had before, technologically
and financially
We can run analytics on that data, run an elastic search, populate a SQL or a NoSQL data warehouse,
and then push this out through APIs to the EHRs and other source transaction systems. We have the
technology around the data that we’ve never had before.
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Lambda Architecture
Lambda Architecture (Figure 11) is a conceptual design supported by the big data world that says
incoming data can be split into two branches: one for batch computations and one for real-
time transactions and computations.
Figure 11: Lambda Architecture
These can be served up to end users in the serving layer underlying all of this historical, as well as results,
storage.
Kappa Architecture
Kappa Architecture (Figure 12) has some appeal. It also comes out of big data in that it uses one incoming
data source and one code set for both real-time and batch-oriented analytics.
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Figure 12: Kappa Architecture (thanks to Julian Forgeat of Google)
Both Lambda and Kappa Architectures can be implemented with a combination of open-source tools, like
Apache Kafka, Apache HBase, Apache Hadoop (HDFS, MapReduce), Apache Spark, Apache Drill,
Spark Streaming, Apache Storm, and Apache Samza. Microsoft and other vendors are blending these
two environments so SQL and NoSQL work together seamlessly.
A few people will say this is too hard or impossible. My response to that is we created a precursor to this
at Northwestern in 2006 without the tools we have today. And just because something is hard doesn’t
mean we shouldn’t do it. Climbing Denali is hard and hiking the Appalachian Trail is time consuming.
They’re both difficult for different reasons. But it doesn’t mean they can’t or shouldn’t be done. The
alternative is not doing it, which ignores all the reasons mentioned earlier for forging ahead. If we don’t,
then we stay in the lower left quadrant of McKinsey’s Digitization Index. As patients and citizens, we
cannot allow this to continue.
Health Catalyst Initial Fabric Services
Some detail into the initial fabric services will provide a sense of how we’re approaching this layer in
DOS:
1. Fabric.Identity and Fabric.Authorization microservices
Fabric.Identity provides authentication (i.e., verifying the user is who he/she is claiming to
be). Fabric.Authorization stores permissions for various user groups and, once given a user, returns the
effective permissions for that user.
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2. Fabric.MachineLearning microservice
A microservice that plugs into a data pipeline (like ours) and runs machine learning models written in R,
Python, and TensorFlow. It encapsulates all the machine learning tools inside so all you need to do is
supply a machine learning model.
3. Fabric.EHR set of microservices
Enables SQL bindings, machine learning models, and application code to show data and insights inside
the EHR workspace using SMART on FHIR.
4. Fabric.PHR set of microservices
Provides the ability to download, share, and update a personal health record. Integrates data from all
available EMRs in a patient’s health ecosystem.
5. Fabric.Terminology set of microservices
Provides the ability for application developers to leverage local and national terminology mapping, and
update services.
6. Fabric.FHIR microservice
A data service that sits on top of any data platform (Health Catalyst EDW, data lake, Hadoop,
etc.). Applications using this data service become portable to any other data platform. It uses data to
FHIR mappings (written in SQL, Hive SQL, etc.) to map data and implements an Analytics on FHIR API
using a cache based on Elasticsearch.
7. Fabric.Telemetry
Provides infrastructure to web and mobile applications to send telemetry data to our Azure cloud, and
provides tools to analyze it using Elasticsearch.
The real-world script example in Figure 13 gives tangible proof of how we are converting our relational
data models into FHIR information models. This is one of the scripts the team has developed for that
conversion.
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Figure 13: Actual example of programming for FHIR mapping (SQL version)
The output into FHIR is shown in Figure 14.
Figure 14: FHIR output from a mapping script
Converting what we have in the relational world into FHIR is difficult, but not impossible. It’s going to be
time consuming, but we can accelerate it.
The Measures Builder Library
We are porting more than 200 Health Catalyst reusable value sets (Figure 15) into a content management
system and code repository called the Measures Builder Library (MBL).
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Figure 15: Sampling of the 200+ Health Catalyst reusable value sets
We can reuse these in Health Catalyst and third-party applications. There are also now more than 2,000
value sets and quality measures from CMS and the National Quality Forum (NQF) library, and this is just
a portion of what we have to measure and keep track of in healthcare. From MBL, we’ll be able to express
those value sets and reuse them in the microservices of the fabric.
The same concept applies with DOS machine learning models. These will reside in the
fabric.machinelearning service described earlier, and any application can invoke these models. Figure
16 shows the Health Catalyst machine learning models in three phases of development.
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Figure 16: Three phases of machine learning models in development for DOS
Figure 17 shows how we manage and reuse the explosion of measures and value sets in the industry
through MBL.
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Figure 17: Measures Builder Library (MBL) is a content management system and set of APIs that
allows registries, value sets, and other measures to be consistently managed, verified, governed, and
reused for application development
Through this system, app developers can reference the 2,000+ NQF and CMS value sets both
programmatically and manually without having to hunt them down. Health Catalyst and our health system
partners will contribute where value sets cannot be automated. The Health Catalyst Precise Registry
Builder will feed MBL, then we will push this out to the Health Catalyst fabric and make it available to our
applications, third-party applications, and client applications.
Role Model Software Development for the Fabric
In addition to building DOS, we want to be role models in software development for the fabric because
this effort should be led by healthcare, not Silicon Valley. Health Catalyst is following these attributes of
role model development to implement and achieve the concepts in DOS:
• Open Source and Collaborative Development: Our code is available on
https://github.com/healthcatalyst. External developers can submit enhancements.
• Open and Modular: All APIs will be publicly published. Customers can pick and choose from the
Health Catalyst components or replace any component with their own or a third party’s.
• Secure by Design: Security services make it easy to build security into any application.
• Microservices Architecture: REST-based services that can be called from web, mobile, or BI
tools.
• Big Data: Leverages big data technologies to provide a high-speed and reliable platform.
• Easy Install and Updates: All services install via Docker.
• Scalable: All services are designed to run in multiple nodes and cluster themselves automatically.
We will know we’ve reached role model status once we can demonstrate these eight software
development vital signs:
1. Successfully implement DOS.
2. Fast, simple releases every two weeks. Constant improvement of our apps.
3. Analytics-driven UI and applications (intelligent user interfaces, driven by situational awareness
of the physician, nurse, patient, etc.).
4. Constantly consuming and expanding the data ecosystem as the enabler of great apps, not apps
as the enabler of data.
5. Machine learning and pattern recognition that clearly amazes all of us with its value to humanity.
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6. Economic scalability. We're so efficient with our products, which work across multiple OS and
data topologies, that it's economically efficient to constantly deploy.
7. Auto-fill analytics. This is a play on words, but how do we, through pattern recognition and
machine learning, anticipate next steps in our partners’ decision making?
8. Google, Facebook, Amazon, and Microsoft come to us for advice about software success and
value.
We will do our best to become this role model.
Ongoing DOS Development and Maintaining Focus
Health Catalyst partners can track development, ask questions, request features, and review roadmaps
and release notes about DOS in the Health Catalyst Community. This is a community effort with a lot of
uphill work ahead.
There will be those who want this to fail because they are afraid of being disrupted and want to protect
the status quo. There will be those who expect failure because of the degree of difficulty and because
healthcare IT doesn’t have a great reputation for breakthrough achievement.
But there are many more who hope DOS succeeds and these are the people we work for. As patients
and members of a global community, this is something we need to do, can do, and will do.
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Dale Sanders, President of TechnologyDale Sanders has been one of the most influential leaders in healthcare analytics and data warehousing since his earliest days in the industry, starting at Intermountain Healthcare from 1997-2005, where he was the chief architect for the enterprise data warehouse (EDW) and regional director of medical informatics at LDS Hospital. In 2001, he founded the Healthcare Data Warehousing Association. From 2005-2009, he was the CIO for Northwestern University’s physicians’ group and the chief architect of the Northwestern Medical EDW. From 2009-2012, he served as the CIO for the national health system of the Cayman Islands where he helped lead the implementation of new care delivery processes that are now associated with accountable care in the US. Prior to his healthcare experience, Dale had a diverse 14-year career that included duties as a CIO on Looking Glass airborne command posts in the US Air Force; IT support for the Reagan/Gorbachev summits; nuclear threat assessment for the National Security Agency and START Treaty; chief architect for the Intel Corp’s Integrated Logistics Data Warehouse; and co-founder of Information Technology International. As a systems engineer at TRW, Dale and his team developed the largest Oracle data warehouse in the world at that time (1995), using an innovative design principle now known as a late binding architecture. He holds a BS degree in chemistry and minor in biology from Ft. Lewis College, Durango Colorado, and is a graduate of the US Air Force Information Systems Engineering program.
About the Author
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3165 East Millrock Drive, Suite 400 Salt Lake City, Utah 84121
ph. 800-309-6800
Copyright © 2017 Health Catalyst
ABOUT HEALTH CATALYST
Health Catalyst is a next-generation data, analytics, and decision support company committed to being a catalyst for massive, sustained improvements in healthcare outcomes. We are the leaders in a new era of advanced predictive analytics for population health and value-based care. with a suite of machine learning-driven solutions, decades of outcomes-improvement expertise, and an unparalleled ability to integrate data from across the healthcare ecosystem. Our proven data warehousing and analytics platform helps improve quality, add efficiency and lower costs in support of more than 85 million patients and growing, ranging from the largest US health system to forward-thinking physician practices. Our technology and professional services can help you keep patients engaged and healthy in their homes and workplaces, and we can help you optimize care delivery to those patients when it becomes necessary. We are grateful to be recognized by Fortune, Gallup, Glassdoor, Modern Healthcare and a host of others as a Best Place to Work in technology and healthcare.
Visit www.healthcatalyst.com, and follow us on Twitter, LinkedIn, and Facebook.