Tips and Tricks for Healthcare Data Management

Dear LinkedIn Community,

We’re glad you are here with us on Velvetech’s IT Talks! ???

Those of you following our newsletters from the very beginning (by the way, thank you) know that Velvetech delivers tech solutions for a range of industries. While our clients come from different business domains like fintech, insurance, manufacturing, or retail — the healthcare field was always the one we felt a deep connection with.?

So, it’s quite natural that our team has become an expert in healthtech development and a trusted partner for many medical organizations. We secretly love everything that drives the industry and helps elevate patient care. Since data is now a central piece of nearly all healthcare operations and it’s hard to imagine any process without it, we want to talk about it today.

Healthcare data privacy, management, analytics, and more will be a leitmotif of this edition. Without further ado, let’s jump to our Q&As.??????

Q1: As the amount and complexity of patient data are increasing, what strategies can help us manage and organize it efficiently??

No one will argue that healthcare revolves around patient data, and its number is constantly increasing indeed. The complexity of data sets, a variety of formats, and diverse sources make data storage and processing tricky.?

A good strategy to start with will be transitioning from paper-based health records to electronic if you haven’t done so yet. EHR solutions streamline data storage, retrieval, and sharing among healthcare providers. Investing in a smart data management platform is another way to make things easier, as it can automate the collection, analysis, and visualization of patient data.??

We also believe that neglecting AI & ML technologies in this context means missing out on effective data workflows. AI in healthcare automates medical data analysis, identifies patterns in it, and generates insights. So, leverage it to get help with clinical decision support, boost preventive care, and essentially personalize medicine.

It might seem not that obvious at first sight, but empowering patients to access and manage their own health data is crucial as well. When they do so through patient portals and mobile apps, it encourages not only data sharing and active participation in healthcare but also promotes data accuracy. And accuracy is a significant part of effective data management.

Q2: How can we maintain compliance and protect patient confidentiality while scaling our healthcare platform??

Well, thinking about it is already half the battle. It means that you apparently know that protecting PHI is crucial. When scaling your healthtech solution, as actually in any other case with patient data, comply with relevant healthcare regulations. Remember about HIPAA and GDPR and healthcare messaging standards like FHIR and HL7. Follow the guidelines for handling patient data in your region.???

It’s a no-brainer, but we can’t skip mentioning it — double down on healthcare data security and implement robust measures. Pay attention to at least encryption and access control. As you scale the platform, the risks of varying threats become graver. Phishing, ransomware and DDoS attacks, data breaches are the top concerns to keep in mind.?

So, make sure to perform regular security audits that will help you identify vulnerabilities and assess compliance with established policies. Combine it with another effective practice of continuous monitoring, as it can detect and respond to any unauthorized access or suspicious activities promptly.??

Q3: What are the ways to use advanced data analytics and machine learning to make patient care better over time??

By design, advanced data analytics and machine learning can drastically enhance patient care, extracting meaningful insights from vast amounts of healthcare data. There are numerous ways today to use these technologies in order to drive improvements in patient care, however, we’ll highlight the most prominent ones.?

• Early disease detection.

Data analytics in healthcare , in particular, predictive analytics helps identify patients at risk of developing certain conditions. Based on historical data, genetic information, lifestyle factors, and clinical indicators, early detection allows for timely intervention and potentially prevents the progression of diseases.

• Personalized treatment plans.?

As we know, machine learning algorithms analyze patient data and identify patterns that can fuel personalized treatment plans. You can tailor treatment recommendations based on patient characteristics, treatment response history, and lifestyle factors — all to create more effective and targeted care.

• Remote patient monitoring.

Most likely, you’ll agree that, as patients or providers, we all love RPM for its efficiency and convenience. Combined with medical IoT , ML algorithms are used for continuous monitoring of patients with chronic conditions. They help healthcare professionals analyze real-time data to detect anomalies, predict exacerbations, and enable proactive interventions.

• Population health management.

Managing population health is another area where we see advanced analytics make waves. It can efficiently identify trends and patterns within patient populations to enable the right response from providers. Specifically, it helps manage high-risk populations, allocate resources strategically, and implement preventive measures to address certain health concerns.

• Readmission risk prediction.

With the average readmission rate of 14.5% in the U.S., it’s natural for hospitals to be willing to reduce it as they are punished with penalties. The technology can step into the game, predicting the likelihood of patient readmission based on historical data, patient characteristics, and post-discharge activities. So, if you’re bothered by high readmission rates, embrace the tech to enable post-discharge care plans and improve patient outcomes.

• Drug discovery and development.

Lastly, we see how AI and ML impact drug discovery. This is especially true for generative AI input as its algorithms analyze biological and chemical data to accelerate the identification of potential therapeutic targets and optimize drug options.?

Q4: What should we focus on to enable our platform to easily share data with external systems and be part of HIE networks??

The short answer is you should focus on these four pillars: interoperability, data standards, security, and compliance. However, this question requires covering more details.

So, to be more specific, your platform should adhere to the key interoperability standards that we mentioned earlier. They are HL7 and FHIR, which facilitate secure and seamless data exchange between healthcare settings and professionals.?

Next, to support data sharing, you want third-party systems to easily integrate with your platform. Thus, the development of APIs that are well-documented and user-friendly is a step to take. Provide comprehensive API documentation, including data formats, authentication mechanisms, and usage guidelines to facilitate integration by external developers. You’ll make their life better.

Additionally, think about robust data mapping and transformation that will ensure data exchanged with other systems aligns with the required standards. Here, various data formats and structures may become a hurdle, so consider implementing data normalization to address it.

If you want to be part of the HIE networks, you, of course, need to actively participate in HIE initiatives and collaborate with other healthcare organizations and providers. This will help stay involved in industry discussions related to healthcare interoperability and keep up with emerging standards and best practices.

We’re almost done with this part, and before wrapping up, we want to emphasize once again that security, access control, and regulatory compliance should be top of mind. But you already know the drill.??

We’re Ready to Answer More of Your Questions??

Thank you for staying with us till the end of this newsletter. We tried our best not to overwhelm you with the content and hope that it was helpful in some way. As healthcare data keeps growing in volume, it urges us to come up with new tools and practices to manage it effectively. Thus, we’ll definitely have more questions to answer in the future. So, don’t hesitate to ask yours.?

If you need any advice about healthcare software development or data management solutions , our team will be happy to assist you. At the end of the day, not technology but human expertise counts the most.