Healthcare data is exploding, from clinical records to wearables, and with it comes both
opportunity and chaos.
In this episode, experts Abhishek Wagle and Anand Rao explore the Healthcare
Dataverse, a digital ecosystem where data engineering, AI, and cloud technologies
converge to improve care, reduce costs, and drive health equity.
Key Highlights:
- How data engineering brings structure and reliability to fragmented healthcare
data.
- Real-world examples of automation improving compliance and reducing errors.
- The growing impact of wearables and patient-generated data on proactive care.
- Inclusive data practices that build trust and support fair treatment
- How AI thrives on clean, complete, and consistent data.
Tune in to explore how clarity in data leads to clarity in care.
0:00
Welcome to Shaping Healthcare, a podcast by Cydia's Tech.
0:04
I'm your host, Laurel Rockel.
0:07
In today's healthcare landscape, data is both essential and incredibly complex.
0:14
From clinical records to wearable devices, the sheer volume and diversity of healthcare data constrain even the most advanced systems.
0:23
But what if we could decode this chaos and turn it into clarity?
0:28
In this episode, we will explore the concept of the Healthcare Dataverse, A dynamic ecosystem where data engineering, AI, and cloud technologies converge to transform patient care, operational efficiency, and HealthEquity.
0:45
Joining me are two experts who bring unique perspectives from the worlds of healthcare delivery and data-driven marketing.
0:51
Abhishek Waghle, Assistant Vice President and Delivery Lead at Citius Tech, drives large scale cloud and professional service projects with a deep expertise in agile, generative, AI and automation.
1:05
We also have Anand Rao, Vice President of Marketing at Data Gaps, A seasoned leader with deep expertise in data management, AIML and modern data infrastructure.
1:16
Anand is known for driving impactful go to marketing strategies and shaping compelling narratives around data quality and innovation.
1:25
Thank you for joining me, gentlemen, and welcome to the podcast.
1:28
Thank you, Laurel.
1:29
Thank you.
1:30
Thanks Laurel.
1:31
Thanks for having me here.
1:32
I'm really excited and looking forward to this podcast.
1:37
Well, thank you for joining me.
1:38
I'm really glad to to have you.
1:40
It's my pleasure and I'm looking forward to our conversation today And and Nan, I would like you to help kick things off for us.
1:47
When we say things like healthcare Dataverse, what exactly do we mean by that and why does this matter now?
1:55
Yeah, absolutely.
1:57
It's a very compelling question to kick off this podcast.
2:03
So I would like to start with, you know, the healthcare data versus basically a digital world of the healthcare data.
2:11
It includes everything from patient records, lab results to insurance claims, doctor lists, fitness tracker data and any other public health reports.
2:23
So it also involves not just the data, but also involves the tools, the systems that collect the data, move it, clean that data, analyze this data, so all the participants, the doctors, the hospitals, the insurance companies can make better decisions.
2:44
So why now?
2:47
Why does it matter now?
2:48
So there's been a huge growth in the data.
2:51
So we're getting more healthcare data than ever before from different applications, different devices, as well as the hospitals themselves.
3:00
So that's great for the different insights, but it also increases the risk of mistakes and bad data causing that.
3:09
So in order to do that, the trust factor is critical.
3:13
So medical and insurance decisions like treatment plans or billing or coverage depend on accurate, reliable data.
3:24
And also there have been several new laws that have been passed that require better quality data, faster reporting, and a clear tracking of all the the data where the where it comes from.
3:41
So AI also depends on it.
3:45
So AI can't work well unless the data it learns from is clean, complete and consistent.
3:53
So what does data worse include?
3:57
The question would come up.
3:59
The first would be data ingestion where you are checking the data when it comes in, making sure that it's complete, it's valid, and then comes the transformation.
4:11
So very fine that the rules and the joining of the different data sources is correct when data is combined or moved.
4:22
And then there's the whole analytics aspect of it, where you have different dashboards and reports and to be sure that you're getting the right results.
4:34
And then there's the whole monitoring of it, where you watch data over time, you get alerts if something looks wrong.
4:43
And there's the whole governance piece of it where you track each piece of data, where it came from, how it's changed, and so on.
4:52
So ultimately the payoff is a healthy data verse in the healthcare space means fewer claim rejections, more accurate reports, better patient outcomes, And that's what it's all about.
5:09
And lower costs to everyone involved because you have cleaner, more trusted data.
5:17
Wow.
5:18
That is, that's certainly a lot.
5:20
That's it's a lot of information.
5:21
That's a lot of moving parts.
5:23
It's a lot to manage.
5:24
And so Abhishek, from your experience, what are some of the biggest challenges that organizations are facing when they're trying to manage all of this healthcare data today?
5:36
Sure, Laura, thanks.
5:38
Thanks for this question.
5:40
I think Anand has really gave us a good start just to extend on top of it, right?
5:46
Healthcare data like we often refer it has a data verse which is a vast interconnected ecosystem of healthcare from Ehrs, Emrs, EM imaging to genomics and patient generated data.
6:00
This ecosystem, as Anand mentioned, holds immense potential to transform care delivery, enable precision medicine and improve patient outcomes.
6:11
But despite its promises, right, the complexity and fragmentation often leads to chaos rather than clarity.
6:20
So being in the healthcare IT space for over 16 years, I feel some of biggest challenges in managing this chaos includes first and foremost data silos as different systems don't talk to each other.
6:35
Then we have uneven adoption of interoperability standards like HL7 Fire.
6:42
Also, another biggest contributor is data quality issues like incomplete, inconsistent or outdated data which undermines the reliability.
6:53
And lastly, to add another layer of complexity right, there are regulations like HIPAA, JDPR and other compliance rules.
7:02
So to put things into perspective in pretty layman terms, imagine a hospital as a city, right?
7:11
With each department is like a neighborhood with its own languages, rules and workflows.
7:19
So in hospital, if you see right, radiology talks in DICOM labs communicate in HL7 and fire and doctors obviously as we all know right, they just love to scribble notes in a free text on a plain paper.
7:35
So getting these departments to collaborate is like trying to coordinate the traffic in a city with broken bridges and traffic signs in different languages.
7:46
So without technical intervention you see this city can't function effectively.
7:51
So engineering this data verse is like a building unique translator that will bring order, accessibility and most important thing meaning to all this madness.
8:05
So yeah, it sounds very complex.
8:09
Chaos is the word that you used a couple times, Madness that that sounds like that.
8:14
It's a lot of information on moving at once and trying to piece that all together to get it to work cohesively.
8:20
So how can Anon, maybe you can help us dig a little deeper into this?
8:24
How can data engineering help bring a deeper clarity to all that chaos?
8:30
And maybe do you have any real world examples?
8:34
Yeah, absolutely.
8:36
So data engineering, as you would expect, helps organize and clean up all this messy healthcare data that Abhishek just talked about by focusing on three main areas.
8:51
The one is the rules, the second is movement, and the third is monitoring.
8:58
So let me go through each of them.
9:00
So first is the rules, and there are clear instructions to tell the system how to check the data.
9:08
For example, making sure that all the required fields are filled, there's no duplicate data that exists, and any numbers or codes that are input are valid.
9:21
So these checks can be reused across different projects in the database.
9:27
The second is movement.
9:29
So data often travels between systems, some of the systems Abhishek just mentioned.
9:35
So data engineers make sure that when it moves from one database to another, it stays accurate and complete.
9:43
They also check for any changes in structure so that nothing breaks.
9:49
And the third aspect, as I mentioned is monitoring.
9:52
So data engineers track data over time to see if something unusual happens, like sudden spikes or missing records.
10:01
So when something looks off, the system automatically alerts the team to fix it quickly.
10:08
To speak of a real world example.
10:12
So a large health insurance company had to send some data files and these would include medical files, pharmacy or any other kind of provider data to over a dozen states.
10:25
So this is AUS healthcare health insurance company and each state had different rules, so missing even one rule could lead to fines from the state.
10:39
So the company built a set of automated templates for each state.
10:44
So these templates checked over 150 rules for each file, separated the errors, created automatic reports and alerts.
10:54
This made the process much faster, more accurate and easy to maintain.
10:59
So even as they added more States and expanded within the US, it was far less maintenance than before.
11:07
So the same approach works when moving data to the cloud.
11:11
So automatic checks can make sure everything matches between the old system and the new system, and AI tools can even help write that code faster.
11:22
And you also can generate synthetic data, which is fake data, but realistic looking that can be used to test the safety without exposing in private information.
11:36
So to sum this up, data engineering turns all this chaos that we just talked about into more organized, reliable and repeatable systems that keeps all this healthcare data clean, safe and trustworthy.
11:53
Yeah, that's, that's an excellent example.
11:56
Thank you very much for for that.
11:58
And, and you can see how these different technologies working together can be a real game changer.
12:04
You mentioned AI cloud platforms and the work that they're doing.
12:09
In your experience, Abhishek, how are these things transforming the healthcare dataverse, right?
12:17
So given the buzz happening around AI, right, I was kind of expecting this question, Lauren.
12:23
So in simple terms, right, AI, artificial intelligence can help in predictive analytics for early diagnosis, natural language processing like NLP, we all call it, as to help extract insights from clinical notes that usually doctors put in right?
12:45
Then next would be image recognition in radiology, personalized medicine using genomics and patient data.
12:52
But most importantly, to make things meaningful, right, it needs clean, well structured data to work effectively.
13:00
Likewise Anand mentioned, right?
13:03
I would say AI here is like the detective in old mystery novels, right?
13:10
Sifting through clues, spotting patterns, and predicting the outcomes.
13:15
Here, in healthcare terms, it is.
13:17
Its diagnosing diseases from images, predicting patient outcomes, and even summarizing the clinical notes for doctors.
13:25
But like any detective, right, it needs good evidence.
13:29
Clean evidence.
13:31
That's where data engineering comes in, ensuring the clues are clean, complete and well connected and to manage such vast data hours.
13:41
Like we all discussed, the cloud is undoubtedly the most suitable and scalable solution that we have in the market right now these days.
13:52
On the flip side of of that, when it's done well, but on the flip side, when it's not done well.
13:58
And then what, what impact do you see on the clinical outcomes?
14:05
Yeah, yeah.
14:06
We have a number of customers who, you know, face all these challenges and then they come to us and we have noticed several patterns.
14:16
So when data engineering is done poorly, it causes mistakes and delays that affect real patient care.
14:24
And I'll try to bring out a few factors.
14:27
So first is either wrong or late information.
14:32
So if data isn't checked carefully, missing or incorrect details like a diagnosis code or a date can sneak into reports.
14:42
So doctors might see the wrong patient risk scores, or contact the wrong people for follow up care, and so on.
14:50
The second factor could be bad calculations.
14:54
So if the rules that organize or calculate the data aren't tested, small errors like wrong date ranges or some of the performance scores are impacted, or payments are impacted.
15:09
So this effects how doctors plan, care, or document the visits.
15:15
The third factor could be broken dashboards.
15:18
So if any report or dashboard isn't tested after software updates, any filters or charts might change without anyone noticing.
15:30
So decisions about staffing or treatment locations could be based on some wrong numbers.
15:38
The next factor could be some missed warning signs.
15:42
So without automatic checks, slow changes like fewer lab tests being done or more readmissions might go unnoticed for several weeks, delaying that patient care.
15:57
And the last would be regulatory problems.
16:00
So if the report sent to the government agencies fail, hospitals might find might face fines or waste time fixing those errors instead of helping the patients.
16:12
So the bottom line is bad data leads to bad decisions.
16:17
Good data engineering like checking, testing, monitoring, that data ensures that doctors have the right information at the right time and that helps patients get the right care faster and keeps the overall healthcare system safe and reliable.
16:34
Yeah, that that data engineering is done well is critical and, and across all these, these different areas of the the healthcare ecosystem and yes, a critical piece, my goodness of, of how many areas that get affected beyond the tech piece, we're also seeing this explosion, this great influx of wearables, the patient generated data.
16:58
How do these new data sources enter the Dataverse, How they get used and how did they help create clarity?
17:07
Great question Lauren.
17:08
So I would like to answer this question bit differently.
17:11
As the healthcare data was expands, the patient generated data from wearables, mobile apps and home monitoring devices is becoming the vital part of the healthcare ecosystem.
17:25
The data provides continuous real world insights that complement the traditional clinical records, helping to detect early risk and personalized care.
17:36
It supports a shift towards preventive and precision medicine by offering a fuller view of patients lifestyle and health system.
17:46
To ensure clarity and usefulness, this data must be standardized, validated and governed with strong privacy and consent frameworks.
17:57
Picture a patient with let's say a hypertension right?
18:01
In real world of traditional care, doctor sees this patient every few months, but with the variables right, we we get daily blood pressure readings, sleep patterns and activity levels.
18:14
So it's like switching from a snapshot every few months to a direct live stream.
18:21
Engineering this system engineering this system to ingest, validate and act on this data is a key to proactive care and it's already saving the lives.
18:33
There are news, right?
18:34
We all have seen wearable devices like Apple Watches giving you early warnings of, let's say increased heart rate or high blood pressure.
18:44
And in addition to this, smart watches, right, there are new things coming like smart rings, remote blood pressure monitors, smart glasses, smart patches for glucose monitoring.
18:56
I feel sky is the limit from here and it will keep on growing.
19:03
The snapshot snapshot versus live stream analogy, I think is a really good one to to make there.
19:09
And yes, it certainly give you more information for a longer period of time and absolutely creating more clarity and helping patients ultimately with with their care.
19:23
Abhishek to to build on this, as the data versus is growing, how do we make sure that it stays inclusive and and incorporates the social determinants of health and makes the system fair for everybody, right?
19:37
So to make the healthcare data worse, right, inclusive and equitable, it must integrate the social determinants of health, including income, housing, and education to provide context.
19:52
Beyond this, all clinical data ensuring diverse data representation helps avoid bias and supports fair treatment across all the populations.
20:05
Ethical practices like transparent consent, privacy protection and community engagement will build trust and accountability.
20:15
And finally, I would say using interoperable and accessible technologies, we need to ensure that data can be shared and understood across systems by all the stakeholders.
20:28
To to help understand this, let's take an example, right?
20:33
Imagine an artificial intelligence model which is only trained on the data from urban hospitals.
20:41
So there are high chances that it might miss important patterns in rural or under served communities.
20:49
So this inclusivity will mean engineering system that represents diverse population languages and conditions.
20:59
It's, it's all about the equity, making sure every pression story right, it's heard, understood and well taken care of.
21:08
Absolutely.
21:09
Gentlemen, this has been such a great conversation with you both today.
21:13
It's been a real pleasure.
21:14
Do you have any closing thoughts as we wrap up our conversation today?
21:20
The only thought I would like to put in is the healthcare data is growing and it is growing at very rapid pace in comparison to any other kind of data, right, that we have all over the world.
21:36
And managing this data, it's going to be challenging.
21:41
But thanks to the technology that is getting developed these days, right, handling this data, making sure every patient history and story is well taken care of, it's it's going to be a, a great thing going forward.
21:58
That's what I feel Any I certainly want to add to that.
22:02
So you don't have to fix everything at once, right?
22:06
You start small, focus where the problems hurt the most.
22:11
You check the data as soon as it comes in.
22:14
Turn your existing rules into some repeatable tests.
22:18
Compare the data as it moves between systems to make sure nothing breaks.
22:22
Add automatic alerts to catch any unusual changes and you share simple reports to everyone to see how good the data really is.
22:32
So all these cloud and AI tools can make things faster, but only if the data is trustworthy.
22:40
So in the end, any strong data engineering helps protect these patients and it makes everything reliable and cuts making sure everything is useful, accurate and ready for what comes next.
22:55
Abhishek.
22:56
And then thank you so much for taking the time to sit down with me today and and share your insights on this this conversation.
23:03
This has been been really great.
23:04
Thank you so much for for joining us today.
23:08
Thank you, Laura, thanks for having us.
23:10
It was a pleasure talking to you.
23:14
The Shaping Healthcare Podcast is brought to you by Cydia's Tech, a leader in healthcare consulting and IT services.
23:20
To find out more about Cydia's Tech, visit citiustech.com.
23:25
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23:33
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23:35
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