7 Tips for Success in Healthcare Data Integration

Feb 15, 2022

Photo of doctors’ hands pointing to schematics of a human brain on a laptop screen. The right information at the right time can mean the difference between life and death. Ninety-six percent of physicians in a Google Cloud survey (The Harris Poll) agreed that easier access to critical data could save lives.

This places a major responsibility on healthcare administrators and health sector IT leaders to provide easy access to well-connected, comprehensive data systems. The systems should help health sector employees use timely, relevant data and information to do their jobs.

Health data is any data related to physical or mental health conditions, reproductive outcomes, causes of death and quality of life of individuals or populations.

Apart from patient data, health data also includes data on delivery of health services, the availability of health manpower and facilities, and the use and costs of resources to the patient. 

Health data may be structured or unstructured.


Structured health data is standardized and easily transferable between systems. Examples are a patient’s name and blood test results, which can be recorded in a structured format such as an Excel file.


Unstructured health data is not standardized, and more challenging to transfer between systems. Examples include text and PDF documents, emails, audio recordings, X-ray scans, written physician notes about a patient, photos, videos, social media posts, and data from mobile devices.


Sources for health data

There are myriad data sources in the health sector. From data on individual patients to data on mass events like pandemics that affect millions, both the volume and diversity of health data are huge. That can make connecting data sources challenging.

For example, data may come from doctor visits, from hospital research centers, from pharmacy prescriptions, from medical lab tests, and even from people’s wearable smart devices.

Besides the volume of sources, data also comes from incompatible systems.


Why data integration matters in healthcare

  • Speed
    Especially in emergency cases, time is of the essence. Good integration enables speedy transmission of useful medical data. You can better identify risk factors and speed up diagnosis. Increasingly, these days, you can also consult a person's Electronic Health Record (EHR), which consolidates much health and medical data about a person in one convenient digital form.

  • Accuracy
    Integrating data enables better evidence-based decision-making, leading to better health outcomes. That’s crucial when it comes to your health. You need an accurate diagnosis, based on accurate information and good quality data, so that you can start the correct course of treatment.

  • Costs

    Integration can enable automation of business processes and more efficient use of resources for reduced costs. From facility operations to clinical treatments, integration helps health care institutions measure the care they deliver, analyze their data, and use data insights to make improvements or changes.

  • Equity

    Getting access to fast, accurate medical data in publicly funded hospitals can mean

    fairer, more equitable health services for all, regardless of age, gender, ethnicity, neighborhood, or income level. Inequity in health services is real and involves many factors; good data connectivity is certainly one of them.


  • System-wide access during emergencies
    The COVID-19 pandemic dramatically showed the need for system-wide access to data in a crisis. The COVID-19 response involved the coordination across international borders of massive amounts of data, such as:

  • infection rates,

  • strategy planning research,

  • hospital and medical staffing, and

  • pharmaceutical research.

  • Improve population health management
    Widespread access to data informs a unified approach in managing public health programs and policies, especially if these have to be monitored, adjusted, or used for analytics. Public health is interdisciplinary, so the data will be coming from diverse fields—epidemiology, community health, biostatistics, mental health, and many others.

The WHO concept of integrated health services

The World Health Organization uses the term “integration” differently from IT technicians. WHO’s definition emphasizes the effective, timely, friendly and cost-effective health outcomes for people:

Integrated health services are the organization and management of health services so that people get the care they need, when they need it, in ways that are user-friendly, achieve the desired results and provide value for money.
—World Health Organization, 2008.

Tips for integrating healthcare data systems.

1. Design your system to gather the right data

By first asking the right questions, data teams can get a better understanding of the healthcare system’s data needs, and redesign it or integrate systems accordingly.

For example, ask:

  • Do you use an Electronic Health Record or an Electronic Medical Record for each patient? If so, how well is that system working for you? Does it need an overhaul?
  • What do doctors and other data users need that your existing systems do not deliver?
  • What are the priorities (think healthcare KPIs) in your specific health system?
    Is it:
    • Improving the quality of patient care?
    • Reducing the death rate?
    • Cost savings on your “patient drug cost per stay” metric?
    • Better use of medical equipment?

Medical dashboards with analytics visualizations can help here—requiring data capture and integration.


2. Go cloud-native and API-first

The large volume of medical data immediately suggests a cloud-native solution.

Meanwhile, the large diversity and geographical distribution of medical data suggests the use of API-enabled architectures. Using APIs enables interoperability. APIs standardize how to connect data, platforms, and services.

It’s especially important that systems work together in medicine. But healthcare data is often fragmented, coming from many sources in many formats. Because there is often no way to standardize data formats in separate systems, a cloud-native, hybrid solution may be best.

Hybrid integration allows a health system to access data in its traditional, on-premises data centers plus use cloud solutions to access and store other kinds of data.


3. Consider cloud data lakes for large-scale storage and real-time access

A data lake is another way to go. It is a storage repository that can hold a vast amount of raw data in its native format until you need it for analytics applications.

Data lakes allow organizations to run analytics without the need to move data to a separate analytics system. However, the challenge with data lakes is that there’s no oversight of the contents. To be useful for wider audiences, data lakes need to have “governance, semantic consistency, and access controls.”

Data lakes let you import any amount of data in real time, from multiple sources—essential for many healthcare services and functions.


4. Be aware of FHIR standards

You can harmonize your structured and unstructured data into standard formats such as the Fast Healthcare Interoperability Resource or FHIR format.

FHIR is a protocol for joining disparate systems together, created with the complexity of healthcare data in mind. It is an internet-first approach to using an XML standard for data structuring. The FHIR standards define how to build APIs.

(For more on FHIR, read “4 Basics to Know about the Role of FHIR in Interoperability.” Since that report in 2016, the FHIR standard has matured and gone global—see the CDC report “What’s Coming in 2022.”)


5. Enable analytics functions

You can connect your data to analytics tools. When you do, ensure your connections between datasets are reliable and scalable. Use Zero Trust standards and principles to secure every endpoint.


6. Deliver data to all the decision-makers

It’s easy to talk about data-driven decision-making, but all too often, the decision-makers in healthcare systems do not have easy or immediate access to the data they need or would like.

A good approach is to design data access systems with a wide range of decision-makers in mind. The infrastructure chosen should fit most people’s needs, rather than building a data solution for just one group of users.

To find a good data infrastructure for a health system, health system leaders can identify a variety of the organization’s most common use cases to inform the creation of analytics solutions for those cases.


7. Ensure security of patient data

With the sharing of medical data comes the risk of violating data privacy regulations.

Aashima Gupta, who spearheads Global Healthcare Solutions in Google Cloud, gives some valuable advice on healthcare transformation in an October 2021 Forbes article. Among her many useful insights is the importance of protecting patient data. She writes:


"Hospitals often use an extract-based approach
that requires many copies of the data outside
of the database—a potential security flaw.
Instead, developers and healthcare professionals
should be able to analyze data where it lives,
all while observing Zero Trust principles
and HIPAA compliance."

Aashima Gupta, Global Healthcare Solutions, Google Cloud

Zero Trust is the practice of authenticating, authorizing, and validating every individual before granting access to data and apps.

About Pixentia

Pixentia is a full-service technology company dedicated to helping clients solve business problems, improve the capability of their people, and achieve better results.

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