Transcript
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Hello everyone.
This is Vishal.
So I'm here to discuss about the predictive data analysis solutions.
I have around two decade of experience in it.
I'm working with Optum and basically in healthcare domain.
So in my career mainly I focused on leveraging technology to improve
operational efficiency and data driven decision making In the healthcare
organization, I am exposed in designing and implementing, then optimizing data
warehouse, tailored for healthcare, and have extensive experience with ET process.
Data modeling and regularity co. That enabling healthcare provider to unlock
actionable insight from complex dataset.
My deep domain knowledge combined with the strong technical expertise in data
architecture, ETL and reporting enable me to bridge the gap between the business
needs and technology solution effectively.
Where it is enhancing solutions, enabling predictive analysis, or
supporting population health management.
I'm passionate about transforming raw data into strategic asset
that drive better outcomes.
So today we are going to discuss about the building scalable t predictive
analytics, data warehouse and healthcare.
So this is how basically to improve the care quality while
optimizing resource utilization.
So when we talk about this topic, let's first discuss that what
exactly it is important and how this is going to work in this scenario
when we talk about data analysis.
This article is basically explored the design and implementation of
predictive analysis in data warehouse and modern hospital management system.
This also alsos the transformation of healthcare operations.
Through sophisticated data management architecture, focusing on the integration
of real time analytics, machine learning, and advanced storage solutions.
It also address the critical challenges in managing the structured
and unstructured medical data, implementing security framework, and
ensuring scalability while maintaining performance standard for the critical
healthcare operations in today's world.
When we are rapidly evolving healthcare landscape, hospitals are experiencing
an digital transformation driven by the imperative to optimize operations.
Enhanced percent care, quality and control is collecting cost.
If you talk about recent studies, as you saw that here we are showing that 30% of
global data, the whole healthcare will represent 30% of all global data by 2025.
That efficiency gain 37.4% and the care quality is 42.8% enhancement in patient
outcomes using analyticals platform.
So the recent studies indicate the healthcare organization implementing
data-driven decision making framework have witnessed a significant 37%
improvement in ultrasound efficiency.
The integration of big data analytics and artificial intelligence in this
healthcare delivery model, with organizations reporting an average
reduction of around administration overhead and a 28.6% increase in resource
utilization efficiency when implementing a comprehensive data warehouse solutions,
this whole healthcare sector, the digital transformation has categorized.
A fundamental shift in how medical institution approach
data management and analysis.
The research indicate that our healthcare organization leveraging
advanced analytics, have achieved a remarkable around 45.3% improvement
in predictive accuracy for percent admission pattern, and around 33%
of enhancement in basically resource allocation efficiency, the implementation
operative analysis in healthcare.
It basically demonstrate substantial impact on percent care outcomes
and operational efficiency.
According to the recent clinical studies, hospitals are utilizing advanced
analytical platform and they have reported a 41.6% reduction in preventable
readmissions, and around 38% improvement in early intervention success rate.
The evaluation of healthcare data warehousing has been marked by
significant technological advancement and methodological innovations.
So when we talk about that, when the healthcare institution implementing modern
data warehouse system that experience around 47% improvement data processing,
and 44% enhancement in real time analytics, the business impact, we can see
the cost reduction resource utilization.
Manufacturer saving and decision making reduction they all are
effectively impact the whole system of the healthcare industry.
If we just implement a better and modern way of data warehousing system.
The evolution of the healthcare data warehousing thing is basically marked.
When we release that in the healthcare institution implementing the modern
warehouse architecture, they can basically experience around 47% improvement in data
processing efficiency, and 44% enhancement in real time analytics capability.
As I told you, by 2025, we have around 30% of the global
data is all about healthcare.
The adoption of cloud-based solutions has further accelerated this transformation,
and that enable around 35% reduction in infrastructure cost and 40%
improvement in system scalability.
These advancement have been particularly impactful in large hospital network, where
integrated data platform have facilitated around 40% improvement in cross facility
coordination, and around 38% enhancement in resource sharing efficiency.
So implementing a predictive analysis in healthcare will demonstrate an
impact on present care outcomes and also the operational efficiency.
This is also shown by all the other studies
when we talk about the architecture analysis.
There are multi-layer architecture visualization here, analytics, air
processing layer, and data storage layer.
So when we talk about this analysis, the foundation of effective healthcare D
House architecture is evolved dramatically within the exponential growth in
healthcare data complexity and volume.
In the recent studies indicate that healthcare Orions are experiencing an
unprecedented search in data generation with estimate suggesting that the
healthcare generates approximate 30% of the world data volume, as
I discussed, projected to read.
Around 2000 or more than that, exabytes by 2025.
The emergence of multi-layer architectural approach has ized
healthcare data management capability.
The research demonstrate that healthcare institutions implementing modern data
warehouse architecture have achieved significant improvement in data
processing efficiency with organizations reporting around 67% reduction in
data retrieval latency, and around 80% improvement in data integration accuracy.
The data injection layer represents a critical component in modern
healthcare data architecture.
Processing and increasing complex area of data types and sources.
When we talk about unstructured data volume are expanding
is even more rapid race.
You can see the data is coming in different formats.
Unstructured data more than rapid race.
You can see 60 to 65% per year.
This layer must accommodate both real-time streaming data.
And percent monitoring system and batch processing of administration
record with leading health constitution reportedly daily injection
volumes exceeding 10 terabytes
hospitals information system.
You can see HIS have evolved to become increasingly sophisticated
with modern platform managing an average of 8,000 distinct data
points per percent encounter.
The research source that integrated HIS platform in healthcare setting
process across, you can say 1.2 million, operation operational
connection daily basis with peak load during admission and discharge period,
reaching out about 3000 ions per minute.
If you talk about the more how the healthcare data growth
and processing metrics, right?
The annual metadata growth rate is around 54%.
We have to talk about the real time processing because we are
talking patient monitoring, resource allocations, emergency
responses, and operational dashboard.
As I said, there are 44.3% improvement in real time analytics
capabilities by just implementing a proper and enhanced version,
a proper and enhanced version of healthcare systems.
So when we talk about the systems we can see our storage layer architecture.
Modern healthcare data warehouse storage architecture have evolved
significantly to address the exponential growth in medical data complexity.
The structural data storage component, leverage modeling optimizes specifically
for the healthcare workload.
And you can see we are implementing star schema normally in healthcare
facilities at implementing a structured data that more than 180,000 daily
percent transaction while maintaining sub-second queries response.
For 88% of anus queries performance analysis from leading healthcare
institution source that properly implemented dimension models can
maintain consistent query performance, even when processing a log queue
data spanning three to five years.
Organizations utilizing modern storage optimization techniques have reported
compressions ratio address of 6.1 for clinical data resulting in substantial
storage efficiency improvement while maintaining critical performance record.
The unstructured data management capabilities have become increasingly
sophisticated as healthcare organization deal with growing volumes of narrative.
Clinical content.
Current implementation shows that unstructured data account for approximate
67% of total healthcare data volume.
With a typical hospitals, you can say
modern document stores have around demonstrate significant improvement
in test processing capabilities and achieve the success rate.
Healthcare organizations utilizing flexible schema design using JSON format
have reported a 39% improvement because data is coming unstructured format a
lot, and it'll basically reduce the time of data processing and manage around
75 to a hundred million documents while daily injection rate of around 15,000.
New document.
In a medium, medium to large healthcare facility, you can see.
The integration between the structured and unru data component through
reference pointers has emerged as a crucial architecture feature
in our analytics layer architecture.
This analytics layer architecture, if you talk about
our analytics layer, if you talk about this layer where the machine learning
models and predictive algorithms come into the picture in this model.
The modern healthcare warehouse has undergone significant
evolution to meet the demanding requirements of healthcare operation.
The studies demonstrate that healthcare, cognition, implementing and hybrid
analytics architecture that achieves a use improvement in support efficiency
and reduction in diagnostic latan.
The stream processing capabilities have evolved to address the complexity of
continuous percent monitoring system.
With current implementation processing around, you can see 18,000 events
per second from connected medical devices and clinical systems.
Our study shows that healthcare organization utilizing advanced stream
processing framework that can identify the critical clinical event around 90%
of accuracy within two 50 milliseconds.
This use the implementation of realtime operational dashboards has re.
Has reorganized the healthcare decision making process.
Healthcare facilities report a 40% improvement in resource
allocation efficiently, and a 38% reduction in response time.
The batch processing infrastructure that handle the complex analytics
workload, that crucial for the healthcare quality, our the ETL workflow in the
current healthcare system, manage an average of 65 million records daily.
With peak processing volume, reaching around 120 million
records during high demand periods.
The organization we are implementing in optimized ETL pipelines that achieve
transformation rates of approximate 1.8 million records per minute.
Machine learning models that train the pipelines have demonstrated
significant advancement in healthcare analytics, the machine learning pipeline
process, and average of eight terabyte of historical percent data daily.
The capabilities for historical trend analysis with modern system
efficiently processing three to five years of historical data compromising
approximately seven 50000000% encounters.
When we talk about the performance optimization and bi, the strategy
Healthcare data warehouse requires is.
Sophisticated optimization strategy to maintain performance at a scale,
the query performance improve and the cloud storage cost will
definitely improve, but it helped to process the record faster.
Modern healthcare facility typically manage between three to five,
five petabytes of archive active data with daily query volume.
Reaching around 350,000 analyticals requests distributed across various
clinical and administrative department.
We are implementing the parting strategies that evolve significantly
with the introduction of machine learning driven partition scheme selection.
Partitioning framework can reduce query execution time by 60% for
times risk analysis queries, which represent approximate 58% of
analytical workload in healthcare.
Healthcare organization implementing is smart.
Partitioning typically between 12 to 50 active partition point table.
But by using the machine learning integration, the diagnostic support,
treatment planning and readmission prevention, we can see more than 40%
improvement in diagnostic accuracy through the machine learning models.
Treatment planning is also improved around 36.7% reduction
in treatment planning time.
Machine learning help us to improve the whole the whole time.
How can we make our time and utilize the time more efficiently?
The response time under 150 milliseconds for 88% of frequently
executed queries when you're using column storage optimization.
That benefited from Advanced com algorithm and integrated data layout
strategy integration with BI tool has evolved through the adoption
of, inte, inte intelligent query routing and casing mechanism.
The data storage optimization, we go with three different format, hot data,
warm data tier, and the core data here.
Hot data means what?
Basically the high performance in memory processing for missing critical present
information requiring instant access.
For example, real time monitoring system, active treatment protocol, or fast
retrieval of emergency clinical decisions.
Warm data.
Basically the balance, performance cost.
SSD stories, high speed.
S-S-D-R-A comprehensive 30 to 90 days clinical histories.
Upcoming appointments.
See during and follow up.
You can see as scholar a warm data, which frequently accessed cold data
is basically cloud-based archival solution, secure cloud archiving.
With compliance certified encryptions, we can regularly compliance records
with tamper proof audit traces.
Data will be saved successfully and it'll be archive.
We are using ING approach, machine learning approach for that.
Data access layer performance optimization has been re ized by
predictive query optimization and intelligence casting strategy.
Healthcare recognition Report that machine learning enhanced view
ization strategy reduce an average report generation time by 58% while
supporting an average of 2,800 concurrent query sessions continuously.
Modern healthcare data warehouse must address complex scalability
challenge while ensuring consistent performance and data accessibility.
Our studies indicate that healthcare organization implementing cloud-based
scalability and framework achieve around one 34% high data processing
throughput, and 71% improved query performance during high demand period.
In the healthcare domain where everything is data.
We have to access the data.
So query optimization must to be improved.
We are implementing the query optimization, indexing strategies,
data partitioning and compression techniques, indexing strategy that
custom healthcare specific indexing patterns like which by which pattern?
The data will be searched a lot.
We can use that pattern to implement indexes to make query performance faster.
Load balancing mechanism have evolved to become.
To become increasing sophisticated in healthcare environment,
the development methodology significantly influence implementation outcomes.
In healthcare settings, organizations start adopting I implementation
strategy and they can report around 65% of higher project success rate.
By time to time, the implementation of cap capacity planning
process have become increasingly critical in health environment.
Because data will be stored and need to access from their ions.
Performance optimization process is evolved a lot to incorporate
healthcare specific requirements.
Organization report that it'll reduce their average execution time and
improve the resource utilization and improve the performance
when you make it.
There is many challenges.
Because data is coming from the structured data, unstructured data use volume
of the data, a lot of data, how to integrate with the EHR system, with a
legacy system, with a security complex, comprehensive, unstructured data.
This all will be taken care if you balance our system right, and create
our system in such a way so that everything will be managed and enhanced.
The query performance we have to implement our roadmap.
Assessment and planning, architecture, design, pilot implementation, and we
have to full development and training.
We have to understand how exactly the architecture matters because a
multi-layer design delivered around 48%.
You can see improvement in processing efficiency.
We have to focus on the primary goal.
Performance will be faster.
Present outcome as a primary goal.
The stakeholder involvement is very necessary.
Build comprehensive into a foundation, not as per afterthought.
So just one thing here, I want to say in the last that the implementation of
predictive analysis data warehouse has fundamentally transformed modern hospital
management by enabling data driven decision making across all operational
aspect healthcare organization.
Adopting these sophisticated architecture frameworks have demonstrated significant
improvement in patient care quality.
Operational efficiency and resource utilization.
The success of these implementation relies on carefully balanced architecture
decisions that address the unique challenges of healthcare data management,
including the integration of diverse data sources, realtime processing
requirement and significant security standard, and the evolution of machine
learning, enhanced optimization strategy.
Coupled with advanced storage solution and analytics capabilities has
created a robust platform capable of supporting both current operational
needs and future scalable requirement.
As healthcare continues to generate increasingly complex and a lot of volume
data, the importance of well-designed predictive analysis data warehouse
becomes even more critical for maintaining high performance healthcare delivery
system while ensuring patient data security and mobility competencies.
So it is very required how to make our system in a much better way.
Thank you so much for joining this session.
Thank you.