Health Data as the Greatest Asset to Transform Care Delivery

Dear LinkedIn Community,

Welcome back to Velvetech’s IT Talks, where every two weeks we cover different tech topics in the form of Q&As.

Today, we all feel that data invades every corner of our lives. It supports our daily decisions, both at work and outside of it, and has become an integral part of business processes across every industry. And it feels that the healthcare sector is among the ones which data benefits most. Why?

Well, probably because health is wealth, and every individual, including doctors themselves, cares about this valuable asset. Paired with the right health technology, data can propel innovation across different medical fields, improve patient experiences and outcomes, and generally enhance the population’s wellbeing and quality of life. That’s a lot.

That’s why in our today’s newsletter, we’ll be talking about healthcare data and the essentials that organizations should know in order to leverage it.     

Let’s get going.

Q1: How to use healthcare data for predictive analytics?

While predictive analytics in healthcare can be a hidden gem for providers and a promising tool for patients, its implementation requires decent knowledge, expertise, and skills. If your goals include using data to improve patient care, optimize resource allocation, or identify trends and patterns, consider the following framework. It will help you turn raw data into a powerful tool. 

1. Collect Data

First, you need to gather various types of healthcare data from different sources you use. This can be EHR, medical imaging, laboratory results, billing records, or wearable devices. Integrate these diverse datasets into a unified data repository.

2. Preprocess Data

The next step is to clean the data to remove any inconsistencies, errors, or missing values. Here you also standardize the data format and ensure compatibility across your data sources.

3. Select Variables

At this point, your team will identify relevant variables that are likely to have predictive power. This may involve clinical metrics, demographic data, health behaviors, etc. It all depends on your needs.

4. Build and Train a Model

Now you can choose appropriate data modeling techniques. Common approaches include regression analysis, classification algorithms, time series analysis, and deep learning methods. So, you build the model, then train and adjust it. Techniques like cross-validation or bootstrapping can come in handy.

5. Test Your Model

Inevitably, your model needs to be tested. There are several appropriate performance metrics for that: accuracy, precision, recall, F1-score, area under the ROC curve, etc.  

6. Deploy the Solution

Finally, it’s time to deploy the trained model into your healthcare system for real-time prediction or decision support. 

Of course, this is just a rough set of steps that your team will follow. It can be expanded depending on the project complexity and goals you want to achieve with predictive analytics. Plus, don’t forget about ethical and regulatory compliance. But you surely know this if you work with data.

Q2: What are the key challenges in the interoperability of healthcare data?

It’s hard to imagine any situation where you don’t need to transfer or process data. Of course, you can always just store it but then it will scarcely be of any value. Thus, the ability of different systems or companies to exchange and use healthcare information seamlessly plays a crucial role in data’s life. 

One of the challenges in interoperability is that data standards and formats vary across systems and institutions, complicating integration. Then, there are also technical disparities between healthcare IT systems that hinder seamless connectivity. The nature of the healthcare ecosystem, in and of itself, is quite fragmented and comprises diverse stakeholders. All that further exacerbates interoperability difficulties. 

Data quality inconsistencies across sources can also impede meaningful data exchange. And stringent privacy regulations like HIPAA pose challenges in sharing data while ensuring compliance. The solution? Collaborative efforts to build user-friendly systems that integrate well with existing workflows. You should also aim to adopt standardized formats, interoperability frameworks, and robust data governance practices. All this will help a lot.

Q3: What are the best practices to consolidate and standardize a data management strategy?

It’s no surprise, but to effectively consolidate and standardize a data management strategy, you need to define clear objectives and align them with the organization's goals. Assessing existing data assets will help you prioritize efforts in accordance with these objectives. 

Ensuring consistency across systems is another important step that helps facilitate seamless data movement. For that, invest in integration technologies, focusing particularly on data integration. There’s also a data governance framework that you need to think about. It plays a significant role in defining policies and standards to store and process data.

To build effective data management, you can’t do without data quality assurance practices.  Implement them to maintain data accuracy and completeness. Plus, ensure proper access controls and security measures to protect sensitive information. Though, this one is quite obvious. 

These are not all the best practices, of course, and there are at least a few more requiring your attention. They include data cataloging and metadata management as well as continuous monitoring of your strategy. In the end, you won’t know what to improve if you don’t keep a close eye on the process. 

Q4: What measures are in place today to ensure the accuracy and integrity of healthcare data?

The answer to this question naturally derives from the previous one. You see, it wouldn’t be false to say that a robust data management strategy is one of the ways to ensure the accuracy and integrity of health data. And thus, practices like data governance and quality assurance should be in place.

However, software like EHR systems is also fundamental for achieving these goals. Just make sure it includes features like validation checks, standardized data entry fields, and automated alerts to reduce errors and increase data consistency. On top of that, interoperability standards, like HL7, FHIR, or DICOM, come to help, ensuring seamless data exchange and integrity. 

While there are many more things that can be listed here, we’d like to touch upon the last one for today — master data management, or MDM. Its goal is to create a single and authoritative source of key data entities, such as patient records, by consolidating and reconciling data from multiple sources. MDM helps eliminate duplicates and ensures consistent data usage across the healthcare organization.

Q5: How do advancements in technology, such as AI and ML, impact healthcare data analysis?

Artificial Intelligence and Machine Learning are those technologies that have drastically changed the healthcare field. Speaking about health data analytics specifically, they made it possible to foresee patient outcomes, such as disease progression and readmission risks. Early intervention and better-informed clinical decisions in general are also the impacts of AI and ML adoption. 

Healthcare AI improves diagnostic analytics, increasing its accuracy, especially in medical imaging. It detects abnormalities and conditions with high precision, sometimes exceeding human capabilities. Personalized medicine benefits from AI and machine learning, too. The algorithms analyze genetic, clinical, and lifestyle data and help medical professionals tailor treatments to individual patients.

AI also plays a crucial role in real-time data analysis from healthcare wearable devices. And that enables continuous monitoring and proactive management of chronic conditions. Moreover, in the field of drug discovery, AI accelerates the process by predicting compound interactions, thus reducing the time and cost involved. 

Finally, data analytics solutions that leverage AI and ML boost operational efficiency. The optimization of patient flows, resource utilization, and appointment scheduling — it’s all the merit of advanced analysis. And there will surely be more use cases in the future.

Learning to Swim in the Pool of Healthcare Data

Data will keep growing in number and provide more opportunities for the medical sector to evolve and generate value. At the same time, we’ll likely see new challenges arise and thus need more tools to overcome them. 

With the right approach and healthcare technology at hand, you can turn digital information into your best friend and counselor. If you don’t know where to start or already have a project idea and need an experienced IT team, drop us a line. Velvetech loves data analytics and has 20+ years of supporting healthcare organizations.