My tool needs to intake data from an EPIC EMR. My understanding is that the hospital can write a script that will push the data to a secure FTP, where I can pull the data down and load it into my system. Is this correct? Also - my understanding is that this data will be in HL7 - is this correct? Thank you for your help!
Firstly , befor pulling data from a hospital EMR get an approval with the hospital's CMO, CIO, CMIO (if they have one) and HIPAA officials.
(HL7 support is a core requirement of every inpatient EMR RFP.)
Two examples: (1) Cerner runs on Oracle and provides both HL7 support and an API to an abstract data layer, but not to the Oracle base tables, and
(2) Epic runs on a MUMPS backend, provides HL7 support, but does not allow direct access to the MUMPS globals.
Once you have the proper permissions and authorizations in place, work with the hospital's IT staff to coordinate real-time HL7 connections between your system and the hospital's EMR. You'll need their help in decoding the structure and contents of certain HL7 messages, the definition of which varies from vendor to vendor.
If you are fortunate, the hospital EMR supports XML-encoded HL7 v3, which provides store and forward data transfers over public networks, and a robust approach to data encoding. But don't count on it.
Capture the incoming HL7 messages into an OLAP database.
Develop software that parses the EMR data from the HL7 messages, transforms the EMR data for loading into a fully normalized (5NF) clinical datawarehouse, then loads the data into the CDW.
EPIC can output to a couple of different database management systems, Oracle and SQL Server are the ones I've heard of being used. The hospital could write a script to push the data to secure FTP, but it doesn't have to be in HL7.
Related
I want to design a large scale web application in the Google cloud and I need a OLAP system that creates ML models which I plan to design by sending all data through Pub/Sub into a BigTable data lake. The models are created by dataproc processes.
The models are deployed to micro services that execute them on data from user sessions. My question is: Where do I store the "normal business data" for this micro services? Do I have to separate the data for the micro services that provide the web application from the data in the data lake, e.g. by using MariaDB instances (db per uS)? Or can I connect them with BigTable?
Regarding the data lake: Are there alternatives to BigTable? Another developer told me that an option is to store data on Google Cloud Storage (Buckets) and access this data with DataProc to save cross-region costs from BigTable.
Wow, lot of questions, lot of hypothesis and lot of possibilities. The best answer is "all depends of your needs"!
Where do I store the "normal business data" for this micro services?
Want do you want to do in these microservices?
Relational data? use relational database like MySQL or PostgreSQL on Cloud SQL
Document oriented storage? Use Firestore or Datastore if the queries on document are "very simple" (very). Else, you can look at partner or marketplace solution like MongoDB Atlas or Elastic
Or can I connect them with BigTable?
Yes you can, but do you need this? If you need the raw data before processing, yes connect to BigTable and query it!
If not, it's better to have a batch process which pre-process the raw data and store only the summary in a relational or document database (better latency for user, but less details)
Are there alternatives to BigTable?
Depends of your needs. BigTable is great for high throughput. If you have less than 1 million of stream write per second, you can consider BigQuery. You can also query BigTable table with BigQuery engine thanks to federated table
BigTable, BigQuery and Cloud Storage are reachable by dataproc, so as you need!
Another developer told me that an option is to store data on Google Cloud Storage (Buckets)
Yes, you can stream to Cloud Storage, but be careful, you don't have checksum validation and thus you can be sure that your data haven't been corrupted.
Note
You can think your application in other way. If you publish event into PubSub, one of common pattern is to process them with Dataflow, at least for the pre-processing -> your dataproc job for training your model will be easier like this!
If you train a Tensorflow model, you can also consider BigQuery ML, not for the training (except if a standard model fit your needs but I doubt), but for the serving part.
Load your tensorflow model into BigQueryML
Simply query your data with BigQuery as input of your model, submit them to your model and get immediately the prediction. That you can store directly into BigQuery with an Insert Select query. The processing for the prediction is free, you pay only the data scanned into BigQuery!
As I said, lot of possibility. Narrow your question to have a sharper answer! Anyway, hope this help
I am new to DW . When we should use the term Datamart and when we should use the term Datawarehousing . Please explain with example may be your own example or in terms of Adventureworks .
I'm don't work on MS SQL Server. But here's a generic example with a business use case.
Let me add another term to this. First off, there is a main transactional database which interacts with your application (assuming you have an application to interact with, obviously). The data gets written into the Master database (hopefully, you are using Master-Slave replication), and simultaneously gets copied into the salve. According to the business and reporting requirements, cleansing and ETL is performed on the application data and data is aggregated and stored in a denormalized form to improve reporting performance and reduce the number of joins. Complex pre-calculated data is readily available to the business user for reporting and analysis purposes. This is a dimensional database - which is a denormalized form of the main transactional database (most probably in 3NF).
But, as you may know, all businesses have different supporting systems which also bring in data in the form of spreadsheets, csvs and flatfiles. This data is usually for a single domain, such as, call center, collections so on and so forth. We can call every such separate domain data as data mart. The data from different domains is also operated upon by an ETL tool and is denormalized in its own fashion. When we combine all the datamarts and dimensional databases for solving reporting and analysis problem for business, we get a data warehouse.
Assume that you have a major application, running on a website - which is your main business. You have all primary consumer interaction on that website. That will give you your primary dimensional database. For consumer support, you may have a separate solution, such as Avaya or Genesys implemented in your company - they will provide you the data on the same (or probably different server). You prepare ETLs to load that data onto your own server. You call the resultant data as data marts. And you combine all of these things to get a data warehouse. I know, I am being repetitive but that is on purpose.
My company is working on a project and we've run into a wall trying to determine the best way to pull the patients ID. We need the patients ID to name the video file for easy searching.
We want to install this system into a bunch of different scan rooms with different MRI's so we think (but we may be wrong) The best way would be to sniff from the network the conversation between the MRI and the server since this would be more standardized.
I know very little about HL7 or how MRI's interact with the server. If you have any knowledge of these protocols I would love to hear from you.
As was already pointed out in a comment - this is more related to DICOM, than HL7.
I assume the MRI machines will store their image data on a PACS server at some point. So the easiest way would be to just query the PACS server via its DICOM interface for MRI studies. The studies have all the patient information embedded in the DICOM image files when they are stored from the MRI to the PACS. There's also a possibility, that You could query the MRI machine itself via its DICOM interface if it happens to provide the necessary Query/Retrieve SCP-s. Information on that can be found in the DICOM conformance statement of the MRI-s. However getting the data from a PACS server would be the easiest and most logical way of achieving it.
There should be DICOM libraries available for all major programming languages and platforms both paid and free. Get one and start exploring!
I have had a request by a client to pull in the Lab Name and CLIA information from several different vendors HL7 feeds. Problem is I am unsure what node I should really pull this information from.
I notice one vendor is using ZPS and it appears they have Lab Name and CLIA there. Although I see that others do not use the ZPS. Just curious what would be the appropriate node to pull these from?
I see the headers nodes look really abbreviated with some of my vendors. I need a perfectly readable name like, 'Johnson Hospital'. Any suggestions on the field you all would use to pull the CLIA and Lab Name?
Welcome to the wild world of HL7. This exact scenario is why interface engines are so prevalent and useful for message exchange in the healthcare industry.
Up until, I believe HL7 v2.5.1, there was no standardization around CLIA identifiers. Assuming you are receiving ORU^R01 message, you may want to look at the segment OBX and field 15, which may have producer or lab identifier. The only thing is that there is a very slim chance that they are using HL7 2.5.1 or are implementing the guidelines as intended. There are a lot of reasons for all of this, but the concept here is that you should be prepared to have to do some work here for each and every integration.
For the data, be prepared to exchange or ask for a technical specification from your trading partner. If that is not a possibility or if they do not have one, you should either ask for a sample export of representative messages from their system or if they maybe have a vendor reference. Since the data that you are looking for is not quite as established as something like an address there is a high likelihood that you will have to get this data from different segments and fields from each trading partner. The ZPS segment that you have in your example, is a good reference. Any segment that starts with Z is a custom segment and was created because the vendor or trading partner could not find a good, existing place to store that data, so they made a new segment to store that data themselves.
For the identifiers, what I would recommend is to create a translation or a mapping table for identifiers. So, if you receive JHOSP or JH123 you can translate/map that to 'Johnson Hospital'. Each EMR or hospital system will have their own way to represent different values and there is no guarantee that they will be consistent, so you must be prepared to handle that scenario.
Is it beneficial to pull the data from Datawarehouse for analytical CRM application or it should be pulled from the source systems without the need of Datawarehouse??....Please help me answering.....
For CRM it is better to fetch the data from datawarehouse. Where a data transformations developed according to the buiness needs using various ETL tools, using this transofrmations you can integrate the CRM analytics for analysing the large chunk of data.
I guess the answer will lie in a few factors
what data you need,
the granularity of that data and,
the ease of extract
If you need data that you will need to access more than one source system, then you will have to do the joining of that data between them. One big strength of getting the data from a DWH, is that they tend to have data from a number of source systems and are well connected across these source systems with busienss rules being applied consistently across them.
A datawarehouse should have lowest granularity data, but sometimes, for pragmatic reasons, decisions may have been taken to partly summarise the data, thus you may not have the approproate granularity.
The big advantage of a DWH is that it is a simle dimensional model structure (for a kimball star schema any how), so as long as the first two are true, I would always get my data from the DWH.
g/l!
Sharing my thoughts on business case to pull from datawarehouse rather than directly from CRM system would be -
DWH can hold lot more indicators for Decision making and analysis at enterprise level across various systems than a single system like CRM. Therefore if you want to further your analysis on CRM data you can merge easily information from other system to perform better analytics/BI from DWH.
If you want to bring conformity across systems for seeing data of customer with single view. For example, you can have pipeline and sales information from CRM and then perform revenue calculation in another system for the same customer. Its possible that you want both sets of details in single place with same customer record linked to both measures.Then you might want to add Risk (Credit information) from external source into the same record in DWH. It brings true scability in terms of reporting and adhoc requests.
Remove the non-core work and dettach the CRM production system from BI and reporting (not talking of specific CRM reports). This has various advantages both terms of operations and convinence. You can google on this subject more to understand the benefits.
For now these are the only points that come to me. I will try adding more thoughts later.
P.S: I am more than happy to be corrected :-)