Getting Started
APIs
FHIR Bulk Data Export
FHIR is normally used to enable access to data one patient or resource at a time, but new FHIR Bulk Data APIs (which use the FHIR
$export operator) are making population level data transfer and analytics possible. There are two main use cases 1upHealth supports.- 1.Export any or all of your FHIR data
- 2.Run population health analytics on top of this population
You can immediately have access to these features once you've signed up with the 1upHealth APIs. You don't need to setup any distributed file stores, analytic query engines, or indexes. You get that all out of the box on 1up.
FHIR Bulk Data analytics
1up supports standard SQL queries run on top of FHIR Bulk Data so that you can run population analytics and queries using tools you're accustomed to (e.g., QuickSight, Looker, SQuirreL, etc.). To see how this analytics query engine works, see our FHIR Analytics documentation.
FHIR $everything Query
FHIR has the option to query everything associated to an individual patient. This is useful when transmitting batch data or getting the full patient history. 1upHealth supports the FHIR
$everything endpoint.FHIR $export Operator
Request the analytics
bulk-data endpoint with the FHIR $export operator to retrieve a list of bulk data files for your client application to download.For FHIR R4 only we support the following search parameters:
Parameter
Description
Example
_type
Allows you to specify which types of resources will be included in the response.
​
For example, _type=Encounter would return only
Encounter resourcesanalytics.{{baseUrl}}/bulk-data/r4/$export?_type=Encounter
​
e.g. analytics.1up.health/bulk-data/r4/$export?_type=Encounter
_since
Resources will only be included in the response if their state has changed after the supplied time (e.g. if Resource.meta.lastUpdated is later than the supplied _since time).
analytics.1up.health/bulk-data/r4/$export?_since=2021-01-01
Sample response output by the Bulk Data API:
1
{
2
"transactionTime": "2021-05-12T16:51:13.561Z",
3
"request": "https://analytics.1up.health/bulk-data/dstu2/$export",
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"requiresAccessToken": true,
5
"output": [
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{
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"type": "AllergyIntolerance",
8
"url": "https://analytics.1up.health/bulk-data/dstu2/$export/AllergyIntolerance/eyJ0eXAiOiJKV1QiLCJhbGciOiJIUzI1NiJ9.eyJzdGFydCI6ImU0NiIsInRvdGFsIjo0LCJ1c2VySWQiOiIxMjM2NzUwNDkifQ.aOqtYWB9Xaup4R80pGjjl4gyaQV9SXRmZaqmnBHp2oI.ndjson"
9
},
10
{
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"type": "Condition",
12
"url": "https://analytics.1up.health/bulk-data/dstu2/$export/Condition/eyJ0eXAiOiJKV1QiLCJhbGciOiJIUzI1NiJ9.eyJzdGFydCI6ImU0NiIsInRvdGFsIjoxMjAxLCJ1c2VySWQiOiIxMjM2NzUwNDkifQ.LcsXvPEnC0vNr1OwIUi4j98qjRxbT-99dr739r7mjrE.ndjson"
13
},
14
{
15
"type": "Procedure",
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"url": "https://analytics.1up.health/bulk-data/dstu2/$export/Procedure/eyJ0eXAiOiJKV1QiLCJhbGciOiJIUzI1NiJ9.eyJzdGFydCI6ImU0NiIsInRvdGFsIjoxMywidXNlcklkIjoiMTIzNjc1MDQ5In0.2qEoEMEzMng0ws33jvWzqLfLQnstluA2BbkDAd1605U.ndjson"
17
},
18
{
19
"type": "MedicationOrder",
20
"url": "https://analytics.1up.health/bulk-data/dstu2/$export/MedicationOrder/eyJ0eXAiOiJKV1QiLCJhbGciOiJIUzI1NiJ9.eyJzdGFydCI6ImU0NiIsInRvdGFsIjo0MSwidXNlcklkIjoiMTIzNjc1MDQ5In0.NdLjPwLte_tvWN5nnaA5YMGw11NrKP_gd6mzXDTeII4.ndjson"
21
},
22
{
23
"type": "MedicationStatement",
24
"url": "https://analytics.1up.health/bulk-data/dstu2/$export/MedicationStatement/eyJ0eXAiOiJKV1QiLCJhbGciOiJIUzI1NiJ9.eyJzdGFydCI6ImU0NiIsInRvdGFsIjo1MywidXNlcklkIjoiMTIzNjc1MDQ5In0.2M1jLqkjN6uR3L9ISsWsJr5S8aj_rNannoHVC2iiEzU.ndjson"
25
},
26
{
27
"type": "Immunization",
28
"url": "https://analytics.1up.health/bulk-data/dstu2/$export/Immunization/eyJ0eXAiOiJKV1QiLCJhbGciOiJIUzI1NiJ9.eyJzdGFydCI6ImU0NiIsInRvdGFsIjo0LCJ1c2VySWQiOiIxMjM2NzUwNDkifQ.aOqtYWB9Xaup4R80pGjjl4gyaQV9SXRmZaqmnBHp2oI.ndjson"
29
},
30
{
31
"type": "Observation",
32
"url": "https://analytics.1up.health/bulk-data/dstu2/$export/Observation/eyJ0eXAiOiJKV1QiLCJhbGciOiJIUzI1NiJ9.eyJzdGFydCI6ImU0NiIsInRvdGFsIjo0MTYsInVzZXJJZCI6IjEyMzY3NTA0OSJ9.9MzsFPH0aWs85fuiFPv7TTSZftbQ2uYqZB1mrvgD_b8.ndjson"
33
},
34
{
35
"type": "DiagnosticReport",
36
"url": "https://analytics.1up.health/bulk-data/dstu2/$export/DiagnosticReport/eyJ0eXAiOiJKV1QiLCJhbGciOiJIUzI1NiJ9.eyJzdGFydCI6ImU0NiIsInRvdGFsIjozLCJ1c2VySWQiOiIxMjM2NzUwNDkifQ.Fpgzt5vbwdtj8ngj7YuOZ_ngZnwZSr0dQunZIrK05c4.ndjson"
37
},
38
{
39
"type": "Patient",
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"url": "https://analytics.1up.health/bulk-data/dstu2/$export/Patient/eyJ0eXAiOiJKV1QiLCJhbGciOiJIUzI1NiJ9.eyJzdGFydCI6ImU0NiIsInRvdGFsIjoxLCJ1c2VySWQiOiIxMjM2NzUwNDkifQ.aiEJoxsOPrr3QMmgVmV2AWUXKIxhREElfFSCHUpRuks.ndjson"
41
},
42
{
43
"type": "RelatedPerson",
44
"url": "https://analytics.1up.health/bulk-data/dstu2/$export/RelatedPerson/eyJ0eXAiOiJKV1QiLCJhbGciOiJIUzI1NiJ9.eyJzdGFydCI6ImU0NiIsInRvdGFsIjoyLCJ1c2VySWQiOiIxMjM2NzUwNDkifQ.tFsPhGTH_LxHwSY8eE9i_DHU9hrxBcFYMjGi5MsjEuU.ndjson"
45
},
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{
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"type": "Encounter",
48
"url": "https://analytics.1up.health/bulk-data/dstu2/$export/Encounter/eyJ0eXAiOiJKV1QiLCJhbGciOiJIUzI1NiJ9.eyJzdGFydCI6ImU0NiIsInRvdGFsIjo0OCwidXNlcklkIjoiMTIzNjc1MDQ5In0.SlnbTyNdWuT_-k_RGHVn6ytWpDrrucuq71FeYvjQijs.ndjson"
49
},
50
{
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"type": "DocumentReference",
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"url": "https://analytics.1up.health/bulk-data/dstu2/$export/DocumentReference/eyJ0eXAiOiJKV1QiLCJhbGciOiJIUzI1NiJ9.eyJzdGFydCI6ImU0NiIsInRvdGFsIjoyODAsInVzZXJJZCI6IjEyMzY3NTA0OSJ9.txQ-_wmL9ybXMrLMprpQZ3heYbsvKYmhHODJQHbzWlI.ndjson"
53
}
54
]
55
}
Copied!
For accessing the .ndjson files an access token is required, this can't be done in a browser, one can inject an access token using API test tools such as Postman.
We Are Experts
Our team is literally setting the standards here. We are balloting the FHIR Bulk Data (i.e., FLAT FHIR) specification through the HL7 standards body along with support from the SMART Health IT team. Additionally, we are building THE reference implementation via the $1M LEAP Grant from the US government in our collaboration with Boston Children's Hospital.
Legislation & Policy
CMS is planning to transform its data pipeline to use FHIR and the FHIR Bulk Data specification. Soon millions of patients' medical claims data will be transmitted using the FHIR Bulk Data APIs. What that will ultimately lead to is most payor / provider relationships will lead to the use of these standard methods of data transfer. This standardization will drastically reduce the esoteric knowledge and interfaces currently required to transmit population level electronic health information.
Use cases
Numerous use cases for bulk electronic health data transfer and analytics can be supported. Many examples solve or improve upon existing needs using a standards based approach and others will unlock the future of healthcare.
- Population health analytics for managing risk or risk adjustments
- Reporting on quality and costs
- Multi EHR or data ware house integrations
- Automating reporting for audits or other partners
- Anonymized research data sets for public health
- Public health surveillance
- Network referrals and leakage analysis
- Calculating HEDIS measures
- Extracting features for machine learning models and, one day, decisions made by artificial intelligent doctor agents
Last modified 1mo ago