Amazon Athena is an interactive query service that makes it easy to analyze data directly in Amazon S3 using standard SQL. With a few clicks in the AWS Management Console, customers can point Athena at their data stored in S3 and begin using standard SQL to run ad-hoc queries and get results in seconds. Athena is serverless, so there is no infrastructure to setup or manage, and customers pay only for the queries they run. You can use Athena to process logs, perform ad-hoc analysis, and run…
$5
per TB of Data Scanned
eClinicalWorks
Score 7.0 out of 10
N/A
eClinicalWorks headquartered in Westborough offers their EHR / EMR solution, which can be upgraded to a full practice management solution at higher pricing tiers.
$449
per month per provider
Google BigQuery
Score 8.8 out of 10
N/A
Google's BigQuery is part of the Google Cloud Platform, a database-as-a-service (DBaaS) supporting the querying and rapid analysis of enterprise data.
$6.25
per TiB (after the 1st 1 TiB per month, which is free)
Compared to every other analytics DB solution I've used, Google BigQuery was by far the easiest to set up and maintain, and scale. The price was also much lower for our use case (internal data analysis).
There are some areas in which this product is better while there are some in which others do better. It's not like Google BigQuery surpasses them in every metric. For a holistic view, I will say we use this because of - scalability, performance, ease of use, and seamless …
BigQuery has a simpler and more intuitive user experience (as is the case with most of its products) compared to AWS, which has a more technical and complex profile, so it was the first tool we used. It's still my go-to option for handling SQL queries, though it doesn't detract …
If you are looking to take a lot of the traditional "database administration" work off someone's plate, going with Amazon Athena certainly has "no code" options to optimize lots of database tasks. I would say this option is less appropriate if you have other Microsoft things at play, such as Power BI.
eClinicalWorks should be used in most medical situations. The program generally speaking works the way it should keeping track of patient records and the like. They have recently added an inpatient module for ASCs. Seems to work pretty well for smaller practices that don't require a lot of additional features or integrations.
Event-based data can be captured seamlessly from our data layers (and exported to Google BigQuery). When events like page-views, clicks, add-to-cart are tracked, Google BigQuery can help efficiently with running queries to observe patterns in user behaviour. That intermediate step of trying to "untangle" event data is resolved by Google BigQuery. A scenario where it could possibly be less appropriate is when analysing "granular" details (like small changes to a database happening very frequently).
One of the strengths of ECW can also be a weakness depending on the user's perception. ECW has a lot of redundancies. There are multiple pathways to perform a task. It can be appealing to advanced computer users because of the versatility. I have found that it tends to confuse lesser experienced computer users.
The creation of templates is very easy and any provider in our system can create one. It definitely makes documentation more efficient. By creating a set of templates for the clinic, we are able to standardize the orders/procedures along established guidelines.
We have converted our scheduling to open access. ECW allows us to set the follow up time and the end of the visit and then an alert is created. Front office staff can run the report and schedule patients closer to the actual time. It has improved our no show/cancellation rates.
GSheet data can be linked to a BigQuery table and the data in that sheet is ingested in realtime into BigQuery. It's a live 'sync' which means it supports insertions, deletions, and alterations. The only limitation here is the schema'; this remains static once the table is created.
Seamless integration with other GCP products.
A simple pipeline might look like this:-
GForms -> GSheets -> BigQuery -> Looker
It all links up really well and with ease.
One instance holds many projects.
Separating data into datamarts or datameshes is really easy in BigQuery, since one BigQuery instance can hold multiple projects; which are isolated collections of datasets.
Meaningful Use Reports should be capturing data in real time and generated fairly quickly instead of the MAQ dashboard extraction process.
Their support teams are not very helpful at certain topics such as the definition/logic of Meaningful Use calculations. These are generally difficult to determine but several cases in regards to Meaningful Use take several days before it gets addressed.
Training videos would be helpful on their support website.
Please expand the availability of documentation, tutorials, and community forums to provide developers with comprehensive support and guidance on using Google BigQuery effectively for their projects.
If possible, simplify the pricing model and provide clearer cost breakdowns to help users understand and plan for expenses when using Google BigQuery. Also, some cost reduction is welcome.
It still misses the process of importing data into Google BigQuery. Probably, by improving compatibility with different data formats and sources and reducing the complexity of data ingestion workflows, it can be made to work.
If we had an option to easily switch to another EMR product we would. However, an EMR keeps you invested solidly in it - once you've started you're then going to be stuck with it. The investment into the data in the system are such that you have no real option to back out of what you are in and move into something else. Again, if we could, we would immediately move to another EMR. The ability to use it and be supported by the vendor has decreased nearly to the point of inability to use.
We have to use this product as its a 3rd party supplier choice to utilise this product for their data side backend so will not be likely we will move away from this product in the future unless the 3rd party supplier decides to change data vendors.
[In my opinion] the features allowed by the system are not designed for providers. [I think] the systems are inefficient, and new features tend to be "bolt on" features either as products purchased and added from other providers or simply a module created and strapped onto the software. There doesn't seem to be much idea around making things easier for the provider, though they like to state that provider burnout is something they are working on.
I think overall it is easy to use. I haven't done anything from the development side but an more of an end user of reporting tables built in Google BigQuery. I connect data visualization tools like Tableau or Power BI to the BigQuery reporting tables to analyze trends and create complex dashboards.
I have never had any significant issues with Google Big Query. It always seems to be up and running properly when I need it. I cannot recall any times where I received any kind of application errors or unplanned outages. If there were any they were resolved quickly by my IT team so I didn't notice them.
I often cannot assign a proper diagnosis under the assessment section; and as mentioned, sometimes (about once a month) the dictation just freezes because "the request has timed out" (even restarting the iPhone/ laptop does not help).
I think Google Big Query's performance is in the acceptable range. Sometimes larger datasets are somewhat sluggish to load but for most of our applications it performs at a reasonable speed. We do have some reports that include a lot of complex calculations and others that run on granular store level data that so sometimes take a bit longer to load which can be frustrating.
You put in support cases through a support portal. [I believe] for no apparent reason, the company decided that their support cannot have access to actual patient records and as a result, it's required that they have to connect remotely to a computer system in our network, and log in as one of our users to do anything. This also entails that they are completely incapable of diagnosing problems and require significant amounts of user input and time to try and begin any sort of work on the problems. [In my opinion] this takes away from patient care and other concerns. Also, while you can put in as detailed a ticket as you want, when you are called, you have to go over the ticket again, as they don't seem to read or care what you put in, as it's more important to them to go over everything in painful detail. Often times you must explain to the tech how the process works. In the past month, we were upgraded overnight with zero warning, which caused issues the following day as we had to update every single computer in our network (over 300) and it requires administrative privileges so couldn't be done by a user. This also doesn't update any information in the programs list, so there's no way to tell whether the update happened or not.
BigQuery can be difficult to support because it is so solid as a product. Many of the issues you will see are related to your own data sets, however you may see issues importing data and managing jobs. If this occurs, it can be a challenge to get to speak to the correct person who can help you.
Paid for training, did not help. They trained prior to go-live, but it was so long ahead that users weren't able to function well when it actually happened, they seemed unable to provide adequate support. [In my experience] further support is typically very boilerplate, and is thus not useful, and has additional cost.
It's very important to limit your schedule during the weeks after go live but it is equally important to have a resource that is the lead at the practice that ensures that milestones are met leading up to the go-live date. Someone must be the point person at the practice otherwise milestones will be missed and the implementation will run into problems.
I was attracted by the final note format of ECW. I said then and still say that most EMR's clinical notes are terrible to try to read and follow in orderly fashion by comparison...BUT the devil is in the data entry and that is where "you live" as a clinician. Incredibly frustrating software because of inflexibility and restrictions of multi level data fields that can only be opened one at a time (i.e. no "toggling" between windows... ooen read and close...then reopen other data entry window....then close and repeat if you need to refer back to original window of data. This applies throughout the software and is due to its reliance on SQL architecture from what I have been told). Kills productivity.
PowerBI can connect to GA4 for example but the data processing is more complicated and it takes longer to create dashboards. Azure is great once the data import has been configured but it's not an easy task for small businesses as it is with BigQuery.
We have continued to expand out use of Google Big Query over the years. I'd say its flexibility and scalability is actually quite good. It also integrates well with other tools like Tableau and Power BI. It has served the needs of multiple data sources across multiple departments within my company.
Google Support has kindly provide individual support and consultants to assist with the integration work. In the circumstance where the consultants are not present to support with the work, Google Support Helpline will always be available to answer to the queries without having to wait for more than 3 days.
I will just share one area that our organization saw the ROI in a very short time period. That is the elimination of a dictation service for most of our specialty group doctors when we introducec Dragon Medical. This functionality brought a tangible benefit and a significant ROI in a short time period.
Previously, running complex queries on our on-premise data warehouse could take hours. Google BigQuery processes the same queries in minutes. We estimate it saves our team at least 25% of their time.
We can target our marketing campaigns very easily and understand our customer behaviour. It lets us personalize marketing campaigns and product recommendations and experience at least a 20% improvement in overall campaign performance.
Now, we only pay for the resources we use. Saved $1 million annually on data infrastructure and data storage costs compared to our previous solution.
