Posts Tagged: data management

Data Management Pain Points in Healthcare

healthcare data pain points

To say data management is challenging in healthcare is an understatement. Many challenges persist for all healthcare data stakeholders. Regulations create compliance requirements, and a disconnect in interoperability makes data sharing burdensome. These data management pain points can add up and may have your organization looking for ways to alleviate them. As pioneers in data management, we’ve learned a lot over the years and want to provide you with some pain-free solutions.

What Are the Most Agonizing Data Management Pain Points?

There are aches and pains all through the ecosystem that touch compliance, interoperability, aggregation, insights, and more.

Compliance Conundrums

Healthcare data requires special care and compliance with HIPAA and other laws. It’s a pain point because it can hinder some areas like interoperability. But the biggest pain is risk and threats. Healthcare is a favorite target of hackers. Healthcare cybersecurity is integral to compliance. That means that any organization that accesses it must have protocols in place along with regular auditing and a strong security posture. 

The Interoperability Pain Point

Without integration of systems, fragmentation is a threat to healthcare data and interoperability. That’s just talking about the internal needs. Sharing data with payers, other providers, public health, and consumers isn’t standardized. Regulators are attempting to remediate this, but the fact that the U.S. health system is so fragmented itself doesn’t help. Until streamlining protocols and practices blanket the entire industry, this will continue to be a pain point. 

Data Aggregation Is Painful

For providers and payers especially, data aggregation can be difficult. With multiple sources of data, combining it all for analysis, sharing, or other activities is often held up by a lack of IT resource bandwidth. Aggregating data enables you to learn more because there’s more context. From a predictive lens, healthcare organizations and patients would benefit from this analysis. There are ways to do this via APIs and analytics engines. This healthcare big data could significantly improve outcomes. 

Moving Data Is Laborious

Just moving data is sometimes an arduous task. Organizations often need to convert data from one health information system (HIS) to another. Others want to retire legacy systems and archive data they still have to retain. 

Not having enough resources causes this healthcare data point. Internal expertise on how to migrate data accurately can also be challenging. There are healthcare data pros like InfoWerks that can help you with this heavy lift. 

What Are Your Healthcare Data Pain Points?

Your data should be improving operations, not hindering you. However, data can only be helpful when it’s accessible, portable, and interoperable. We’re experts at all three and make data management pain-free. Contact our team today to discuss your healthcare data pain points. 

Medical Data Management Challenges and Solutions

medical data management

Medical data management may only be three words, but it’s so complex! Most all healthcare organizations deal solely with digital data. The volume keeps growing, regulations are constantly changing, and exchanging it continues to be cumbersome. 

If you’re struggling with managing your medical data, we’re going to break down the top challenges and provide some practical solutions.

What Is Medical Data Management?

Generally speaking, it describes the organization of healthcare data and how you use it. It can include everything from what’s in EHRs to pharmacy systems to other health information systems (HIS). While the principle is simple, the execution is not. Let’s talk about those challenges.

Challenges with Medical Data Management 

Fragmented Data

Within your organization, you may have multiple EHRs and HIS. That means data lives in different silos. Further, there are many formats, some of which are specialized. Often, this means you don’t have a single source of truth. Systems can’t talk to each other, so having a holistic view of patient information is difficult. 

Solving Fragmented Data

  • Striving toward interoperability: The road to healthcare interoperability has many potholes because standardization is still lacking. Without integrated systems, fragmentation remains. Organizations should develop a strategic roadmap to at least have internal interoperability.
  • Purging duplicates and old data: Your systems may have lots of duplicate information, or it may be inaccurate and stale. Now, you can’t just delete everything due to medical record retention laws. However, you can clean your data by removing duplicates and removing anything that’s no longer necessary to keep legally.

The Volume Is Staggering and Causing Performance Issues

The more data in your software, the more strain on them. Each day, clinicians and others add more and more. That can overwhelm systems and could increase your costs for hosting it. The question becomes, do you need it all in your active system?

Solving Volume Issues

The best way to decrease the volume is to archive what’s inactive but cannot be purged. If the requirements say that you have to keep those records, yet you know the person is no longer a patient, then move it to a secure, cloud-based archive. You can access it if you need it, but it’s not impacting your platforms. Data archiving is a cost-effective and efficient way to reduce volume. 

Deriving Insights from Data

Patient data is valuable for many reasons. First, it helps improve care for that specific individual. Second, you can anonymize the data and use it in predictive models for various end uses. Those could include:

  • Public health initiatives (i.e., databases regarding COVID-19 infections, treatment, and long-term symptoms).
  • Determining organizational performance (i.e., how long does it take to see patients or other questions).
  • Predictive modeling regarding supplies (i.e., finding out the most in-demand prescriptions are so you can stock better). 

Solving the Analytics Problem

To unravel this challenge requires several components. You’ll need a secure way to aggregate data and share it within platforms. Then you’ll need an analytics engine to do the assessment. There are lots of tools that can do this that use AI and machine learning. That’s where the future of any data management is heading. Custom data solutions will likely be necessary. 

Regulations and Compliance

Medical data is special, and there are lots of laws that tell you how to collect and use it. The central law is HIPAA, but others impact healthcare data. 

Every healthcare entity must balance compliance with usability. Most have workflows and protocols that mark off every part of a HIPAA compliance checklist. That doesn’t mean you don’t have challenges, especially when new regulations come into play like the Interoperability Rule.

Solving Regulatory Concerns

The best approach to remain compliant is to have internal safeguards, audits, and training. However, you also have to make sure the vendors you work with follow the rules. Do your due diligence before you engage with a partner that will have access to patient data. 

What Are Your Medical Data Management Challenges?

No matter the healthcare organization, you likely have challenges with managing medical data. Whatever those might be, our healthcare data experts are here to help. Get in touch today to see how we can be problem-solvers for you. 

What Healthcare Organizations Need to Know About Data Migrations

data migrations

Data migrations are a normal part of healthcare operations. Hospitals, healthcare systems, pharmacies, and providers change health information systems (HIS). This process includes much more than removing data from one system and ingesting it into another. There are considerations for security, privacy, and accuracy. 

In our 23 years of migrating healthcare data, we’ve developed a series of best practices. Now, we’re sharing them with you. The more you know, the less stressful the process!

Migration Types

Depending on your needs, the migration may have different parameters. There are two main paths for migration: converting to a completely new target system or pushing data to existing ones.

A New Target System

This scenario describes the complete sunset of the existing system. You want to convert existing data, within a specific range, to an “empty” system. This option doesn’t mean you migrate all the data from one system. You can choose to archive what’s not current, based on the definition. 

An Existing System

This type of migration is typical during an acquisition or consolidation. You need to move data from one system into your system that’s in use.

In either of these circumstances, a substantial conversion workflow should be in place. Here’s what we want healthcare professionals to know.

Purging Is a Good Idea

If you have data points in your EHR or pharmacy system, you likely have unconnected data. The data is not attached to a record. Purging this type of data makes the migration easier.

Additionally, you may want to filter out inactive patients. Now, you still must adhere to medical record retention requirements. You can do that with an archiving tool, instead of pushing it to your new system.

Patient Matching

healthcare data migration

If you are moving data from a system to an existing system, the patient may already exist. This concern is likely to occur when there is an internal consolidation. You may have been using multiple HIS platforms that each has a patient profile. Patient matching can include fields for name, date of birth, and SSN.

You’ll need to work with your data migration partner to establish ways to merge these files. Otherwise, you’ll have a duplicate problem.

Field Matching

Even though HIS software uses many of the same fields. It’s rarely a one to one match. Rather, the process requires advanced programming skills to ensure accuracy. Further, fields can be misused. If that’s the case, it’s another challenge to resolve.

Systems also use codes, abbreviations, and other non-standard information. There are multiple fields with structured and unstructured data that require analysis. From that analysis, you can develop the right migration plan. 


One of the most critical steps in migrations is data validation. You and your new software vendor should review how the test data looks in the new system. This is a time to verify that field matching is accurate. Never move on to go-live without validation.


When you begin a project, you’ll have a preferred go-live date. To meet that go-live, you have to work back from it. There are lots of opportunities to get off schedule. That’s why we provide our implementation guide. It marks every step in the process. It also defines roles and responsibilities. From the beginning, we set up clear expectations. Meeting your go-live date is just as important for us, as it is to you.

Pain-Free Data Migrations 

We’ve been perfecting healthcare data migrations for over 23 years. With our healthcare-centric approach, we ensure your data is portable, accurate, and accessible. Learn more about our process today.