Healthcare, indeed, is a complex domain to unravel but it is not impossible. Over time, clinics and hospitals have gathered different technologies to solve different aspects of clinical or medical problems. Now that analytics is the norm of running a more impactful business, the challenge of working with disintegrated applications with unstructured data is more evident.
Here are some use cases of data and analytics in Radiology:
1. Data Democratization
Simply put, data democratization means that data is easily accessible to end-users in a usable form whenever they need it. People don’t have to go through any tedious process to get the data required to work with.
That sounds very simple, right?
Yes, but a lot goes into the process of achieving it. It doesn’t have to be done at once. It can be done in phases and it is very important to consider this an investment. Data democratization is the bedrock for a comprehensive implementation of analytics.
a) Master Data Management (MDM)
MDM is a whole project on its own. MDM ensures that business metrics and entities are standardized and interpreted the same way in the organization. For example, a location can have different location codes and names across many applications – scheduling, billing, etc. MDM will ensure a standardized name is first decided on by stakeholders then all applications begin to use that name. If applications cannot use the standardized name, a mapping can be done to indicate that the current name is the same as the standardized one. The significant effort here is to ensure a seamless process is in place to achieve this.
b) Data Architecture and Engineering
This is another big chunk in the entire process of data democratization. Data architecture ensures that there is an infrastructure suitable for high availability, performance, and security of data and data processing. The architecture supports other data processing tasks like ETL/ELT (Extract Load Transform), machine learning modeling, MDM and Analytics.
Data Engineering is the process of collecting data from multiple sources, sometimes in different formats and processing it in such a way that other users such as Data Scientists and other Analysts can have a validated and optimized view of transformed data.
c) Analytics Interface
The whole essence of data democratization is to have quick access to data. Not only should data be easily accessible, but it should be easy to understand and gain insights from and so, having a technology that meets these goals is important. Whatever technology is used, it also should not compromise on data security, governance, and audit.
2. Measuring Quality of Care
As the saying goes – you cannot improve what you cannot measure.
Healthcare is all about providing care and ensuring it is of good quality is crucial. Developing a basis to measure healthcare should be without sentiments even though a patient’s feedback is just as important.
Gather or create applicable data sources, process the data and plug into a business intelligence tool to stay informed, make projections, and be more proactive in implementing actions. These measures can be analyzed in different dimensions and at different frequencies to gauge overall competency and performance of the entire practice or for each care provider.
Here are some measures that can be used:
a) Structural Measures:
This measures how the organization is set up to ensure it can provide quality care. For example, care provider’s credentialing, screening methods for more accurate diagnosis.
b) Outcome Measures:
The actual impact/outcome of a diagnosis or treatment given to a patient. For example, a patient with a cancer diagnosis found to be false or vice versa.
With this information at your fingertips, further analysis can be done to examine how structural measures can impact outcomes and the level of investment the company can make for an optimal ROI.
3. Operational and Efficiency Throughput
There is a typical patient encounter workflow. Having a full grasp of this workflow is so crucial for optimizing operations. It will improve customer’s experience, generally increase operational efficiency, and reduce overheads.
In most cases, different applications manage these subsets of patient engagements so the challenge of integrating these disparate sources can be handled with data engineering. Structure the data to track side-by-side schedule, no-show, cancellation, admission and exam completion rates across locations or exam types. With this, you can identify peculiar problems with certain exams or locations and monitor how well you are tracking your actual volume with your projections.
Learn More: [Case Study] Operational & Efficiency Throughput
4. Predictive Modeling
With enough insights gathered from processed data, take it up a notch with some of these predictive models:
a) Reduced financial loss with a prediction of no-shows
b) Optimal capacity utilization with demand forecasting
c) Improved productivity and reduced overhead costs with predictive machine maintenance
d) Natural language processing on dictate reports
e) Cancer classification based on imaging features
The applications of data and analytics in the healthcare industry are endless. Talk to Syntelli about your Healthcare organization’s challenges, and let our experts help.
Moyosore Lawal, Sr Analytics Associate
Providing solutions that enhance business competitiveness and enable companies achieve their goals leveraging on data is what Moyo stands for. She has worked with data in a number of ways and has a well-grounded understanding of the data lifecycle.
As a Data Scientist/Engineer, she has managed several successful projects building and implementing predictive models. She earned her M.S. in Data Science and Business Analytics degree from the University of North Carolina at Charlotte.
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