Healthcare Data Integration of Multiple PM Systems: Overcoming a Common MSO Challenge

Uber, Redfin, and Netflix are the envy of American capitalism. Fueling their success is their brilliant use of customer data which allows them to deliver the utmost in convenient, personalized services.

Could management services organizations (MSOs) handling multiple PM systems achieve the same level of customer service? 

Clear, convenient, and personalized. It’s what the 21st-century consumer wants. The consumer doesn’t care how tough it is to integrate a dozen different PM systems. They just want to get great care without too much hassle.   

The healthcare version of faster rides, ideal houses, and better movies could look like speedier patient intake, easier communication, less confusion, and most of all – better health outcomes. It takes healthcare data integration to get this all done. And big healthcare systems have done it.

• Johns Hopkins uses individual patient data and characteristics to tailor medical treatments and procedures to individual patients. Their patient satisfaction rates stay 20 percent higher than national and state averages year after year. 

• Intermountain Healthcare integrates clinical, financial, and operational data from across its Utah health system. This program helped them achieve a 21 percent drop in heart failure readmissions, $30 million in annual savings via an optimized supply chain, and robust preventative care strategies for high-risk patients. 

You, too, can use data to start working toward better patient convenience, loyalty, and health. MSOs that get a grasp on data sources, hammer out system interoperability, and centralize data for robust analysis stand to reap many clinical, administrative, reputational, and financial benefits.

Getting there takes interoperability and healthcare data integration. 

What is healthcare data integration? 

Healthcare data integration is the process of consolidating and unifying diverse health-related data from multiple sources into a cohesive, comprehensive system. It combines information from electronic health records (EHRs), medical devices, laboratory systems, pharmacy databases, billing systems, patient portals, and more. The goal is to create a single, unified view of patient information that is accessible, accurate, and up-to-date.

Integration gives healthcare providers a complete picture of a patient's health history, treatments, and outcomes, leading to more informed clinical decisions and personalized care plans. Additionally, healthcare data integration supports better operations (including the revenue cycle) and enables more effective care coordination across different healthcare settings. As the healthcare industry continues to digitize and generate vast amounts of data, effective integration becomes increasingly crucial for delivering high-quality, patient-centered care while optimizing resource utilization and reducing costs.

MSOs and data integration

Managing multiple physician groups and locations, MSOs have an even greater responsibility to curate data responsibly. Managers stand at the nexus of vast amounts of healthcare data coming from diverse sources, making effective data management and integration not just beneficial, but essential.

By integrating data across all acquisitions, MSOs can create a comprehensive view of patient care, operational efficiency, and financial performance. A holistic perspective helps them identify best practices, streamline operations, and implement standardized protocols that can improve patient outcomes and reduce costs. Moreover, integrated data allows MSOs to leverage economies of scale in analytics and reporting, providing valuable insights that individual practices might not be able to generate on their own.

Beyond these benefits, MSOs must take into account that, as healthcare continues to move towards value-based care models, they will need thorough data aggregation and analysis to demonstrate quality metrics and outcomes. Integrated data systems allow MSOs to track performance, identify areas for improvement, and implement targeted interventions. This capability helps MSOs retain more power in payer contracting, participate in quality improvement initiatives, and ensure compliance with regulatory requirements. By effectively managing and integrating data, MSOs can position themselves and their affiliated providers to thrive.

Why a robust healthcare data management system is critical now  

Big data began pouring into practices and physician groups with the federal government’s Health Information Technology for Economic and Clinical Health (HITECH) Act of 2009.   

The healthcare industry’s data generation swells every day. In 2020, global healthcare produced 2.3 zettabytes of data daily. A single hospital can generate approximately 50 petabytes of data in just one day or twice the entire data holdings of the Library of Congress. This exponential growth in healthcare data underscores the critical need for robust data management and integration strategies in healthcare.

Just as we’ve caught our breath on PM use, along comes wearable technology and the Internet of things (IoT) to gather more data. Adding this data to that coming from the PMS makes the volume of healthcare data so vast it must now be measured in zettabytes or 1 billion terabytes, which is 1,000 gigabytes. We’ve come a long way from the 1-megabyte floppy disks that characterized the Information Age in the 1980s. 

Constant patient monitoring via wearable technology and the IoT will become standard, as consumer wearables converge with medical technology. Available today or in the works are earbuds that measure your core temperature, socks that can monitor a baby’s heart rate, and a sports bra equipped with sensors to detect cancer. Experts are referring to the data generated from these technologies as an individual's “data-ome,” a concept illustrated in the image below. 

‍

‍

What healthcare data integration can do for the revenue cycle

Establishing a uniform data integration strategy not only helps the MSO modernize its acquisitions for the long term, but it also improves reputation and revenue right away. 

With systemic data integration, the MSO helps the physician group or practice: 

• improve patient health outcomes. Data integration helps ensure every intervention is based on the complete patient’s medical history and characteristics. 
• improve care coordination across multiple facilities
• analyze key performance indicators (KPIs) because financial and administrative records exist in one place. 
• improve claims accuracy, thereby reducing denials.
• reduce manual data entry and reconciliation with automated data transfer, real-time updates across systems, and the elimination of duplicate entries. 
• improve staff productivity.
• improve leader decision-making by providing a comprehensive view of operations, patient care, and outcomes. 
• make patient information easier to use.
• make patient data more accurate. 

The bottom line is that adequate data centralization, management, and analysis reveal exactly what’s going on with individual patients, patient populations, facility operations, and revenue cycle. These are insights the MSO and its acquisitions can start acting on right away. 

System interoperability and data integration

Before we can derive insights from data integration, however, we have to make sure all of our data-gathering systems – software – communicate properly.  The ambulatory surgical center needs to communicate clearly with or “speak the same language of” the hospital and the long-term care facility. The PMS has to make sure its data can be understood or translated by the patient portal and telemedicine platform. Radiology data must have the same structure as the referring hospital. This bidirectional communication takes reliable interoperability. 

Three out of four healthcare executives now rank data interoperability as the highest or one of the highest priorities for their organization, according to Google Cloud. 

What is healthcare data interoperability?

Healthcare interoperability is the ability of different healthcare information technology systems and software applications to communicate, exchange data, and use the exchanged information. It allows various healthcare providers, organizations, and systems to seamlessly share and interpret patient data and other health-related information, regardless of the system or software used.

Healthcare interoperability has been an issue of contention for years, but regulations like the 21st Century Cures Act and the Interoperability and Patient Access Final Rule are pushing the industry towards greater interoperability. While some healthcare organizations have achieved advanced levels of interoperability, others are still working on it. Luckily, emerging technologies like cloud computing, APIs, and blockchain are accelerating the adoption of interoperability systems. Once an MSO or physician group achieves sufficient interoperability, it can start the data integration process, no matter how many PM systems it’s dealing with.   

Interoperability typically involves three levels: foundational, structural, and semantic. 

• Foundational interoperability - simple data exchange between systems without interpretation. The receiving systems can acknowledge receipt but don't necessarily understand the content.some text
  • Example: Sending a PDF document containing patient information that can be stored and read, but not automatically processed by the receiving system
• Structural interoperability - defines the format, syntax, and organization of data exchange. It ensures data is preserved in a recognizable and usable form for the receiving system. By relying on message format standards like HL7 or FHIR, it maintains the clinical or operational meaning of the data.some text
  • Example: ePrescribing, where systems use the same data standards for prescription elements
• Semantic interoperability - can handle heterogeneously represented data with different structures or terminology. It ensures the receiving system understands, even if its algorithms are unknown to the sending system. some text
  • Example:  when a patient's blood pressure reading is taken at a primary care clinic and shared with a specialist at a hospital. The receiving system not only recognizes the numerical values but also understands that they represent systolic and diastolic blood pressure, their units of measurement (e.g., mmHg), and can automatically categorize the reading as normal, elevated, or high based on standardized clinical guidelines.  

Healthcare interoperability v. data integration

Healthcare interoperability and healthcare data integration are related but distinct concepts within healthcare IT. 

Interoperability refers to the ability of different healthcare information systems, devices, and applications to communicate, exchange data, and use the information effectively. 

On the other hand, data integration is the consolidation and unification of data from various sources within a healthcare organization or across different organizations. Data integration involves collecting, cleaning, transforming, and cohesively presenting data, possibly using data visualization. It conducts data extraction, transformation, and loading (ETL) to create one, unified dataset. These actions result in a comprehensive, consolidated view of patient information or organizational data.

Overall, interoperability enables the exchange of data between systems, while data integration helps consolidate and make sense of that data for improved decision-making, operations, and patient care. In practice, both interoperability and data integration are crucial for creating a comprehensive and efficient healthcare IT ecosystem. 

How to ensure your system is fully interoperable

Despite the government mandates touched on above, many healthcare organizations are missing out on the benefits of interoperability and data integration. Take these steps to clean up your data from the many PM systems you manage. 

1. Adopt standards: Implement widely accepted healthcare data standards like HL7 or FHIR (Fast Healthcare Interoperability Resources) to ensure consistent data formatting and exchange.
2. Invest in modern infrastructure: Update legacy systems and invest in cloud-based solutions designed with interoperability in mind.
3. Prioritize data governance: Establish clear policies and procedures for data management, quality, and security across the organization.
4. Collaborate with partners: Work closely with other healthcare providers, payers, and technology vendors to ensure seamless data exchange.
5. Focus on semantic interoperability: Go beyond just exchanging data to ensure that the meaning of the information is preserved and understood across systems.
6. Implement strong security measures: Ensure that data sharing is secure and compliant with regulations like HIPAA.
7. Train staff: Provide comprehensive training to ensure that staff understand the importance of interoperability and how to use interoperable systems effectively.
8. Participate in health information exchanges (HIEs): Join regional or national HIEs to facilitate broader data sharing.
9. Stay informed about data and compliance regulations: Keep up-to-date with evolving interoperability regulations and ensure compliance.
10. Conduct regular assessments: Periodically evaluate the organization's interoperability capabilities and identify areas for improvement.

As healthcare organizations integrate diverse systems -- from electronic health records (EHRs) to billing systems and diagnostic tools – the demand for robust interoperability standards and protocols increases. The task of ensuring interoperability falls to your IT staff or an outside consultant. 

Challenges to data integrations with an MSO’s Multiple PM systems

As more management services organizations purchase external clinics, physician practices, and hospitals, chief financial officers will juggle more and more PM systems. The complexity can be overwhelming. Expect these obstacles: 

Providers’ legacy systems:  Older software systems may lack open APIs or process data in legacy formats. It will take your IT team a few more steps to integrate. 

Lack of standardized data formats:  80 percent of medical data generated today,  exists in unstructured formats such as medical images, audio recordings of clinical notes, and PDF reports. Unlike structured data that can be neatly organized into traditional row-column databases, unstructured data presents unique storage and analysis challenges.

The complexity of unstructured data makes it resistant to standard querying methods. Self-service analytical tools, which are typically designed to work with structured data, cannot effectively process or analyze this unstructured information. This limitation poses significant challenges for healthcare professionals and researchers who need to extract meaningful insights from the full spectrum of available medical data.

You can make unstructured data accessible by transforming it into an appropriate format. It must be labeled, anonymized (if necessary), and securely uploaded to a “data lake,” - a centralized repository of raw, unstructured, and structured data in its native format.  

Data privacy, breaches, and security:  Sensitive patient information draws hackers and criminals. Ensuring the security of integrated systems is crucial. All healthcare entities must have strong security measures and continuous monitoring. 

Regulatory Compliance and Interoperability:  Adhering to HIPAA regulations is essential for protecting patient privacy. Additionally, integrated systems must comply with interoperability standards like FHIR and HL7 to facilitate efficient and secure data sharing among providers. This combination of compliance and standardization underlies seamless health information exchange. MSOs must always consider which data assets can be exchanged internally and / or shared with authorized third parties (e.g., clinical research partners). Accidental data disclosures and public data breaches can trigger substantial regulatory penalties.

Interoperability Challenges:  With often dozens of existing systems and data formats, getting all to accept one standard communication protocol is challenging. As mentioned above, The 21st Century Cures Act has made efforts to establish common industry standards for healthcare data, but its impact remains limited. In the current landscape, healthcare organizations are often forced to develop their own custom data integration solutions and system connectors when standardized or native options are unavailable.

Integration Costs and Complexity:  Implementation can be costly and complicated, despite benefits. Integrating existing systems, especially older ones, can be expensive. Organizations must invest in new technologies, training, and sometimes entirely new systems.

Workflow Complexities: Creating streamlined workflows that maintain all aspects of patient care during integration can be challenging.  Further, finding people who have both general engineering and healthcare knowledge also poses challenges. 

Ongoing Maintenance:  An integrated system requires regular updates and maintenance, a significant ongoing expense. 

While healthcare data integration promises to revolutionize patient care and operational efficiency, it also presents real and multifaceted challenges. Awareness of these issues prepares you to tackle what will become your data goldmine. Most gold miners didn’t get rich in a day! 

Data integration with multiple PM systems

The first task is to convert acquisitiojn's PM systems to the one you've chosen. Our PMS integration post covers how to work with your acquisitions before, during, and after integration to make the process as painlessly as possible.   

Integrating your acquisitions’ PM systems involves consolidating contact information, technical specifications, and unique terminologies from each system. Fatigued by constant technology upgrades, staff at newly acquired practices and physician groups may drag their feet in carrying out these tasks. The integration process is further complicated by data incompatibility issues, migration risks, and compliance concerns that arise when incorporating smaller entities into a larger organization.

Given these challenges, the PMS integration process can take several months and involve multiple stakeholders from each acquisition. Progress may vary among different acquisitions. However, the long-term benefits for both the providers and the MSO make the struggle worthwhile. 

If you encounter initial resistance, share with the physician group that the new PMS has:

• enhanced features such as telehealth integration, population health management, AI-driven decision support, improved workflows, comprehensive reporting and analytics, and remote access capabilities.

• the analytics to track and improve performance. 

• bi-directional interoperability to simplify communication with payers, referrals, hospitals, and labs.  

In our experience, provider physicians and staff typically come to appreciate access to such advanced systems. Furthermore, having all acquisitions on one PMS creates significant benefits for the MSO. As you gain experience with the PM system across multiple practices, you become more effective at addressing and resolving issues. Lastly, the consolidated use of a single PMS system across numerous practices gives you greater negotiating power and improved customer service from the PMS vendor, ultimately benefiting your affiliated providers.

How MSOs carry out healthcare data integration

Of course, “a journey of 1,000 miles begins with the first step,” even for MSOs. Here, we break down the steps you need to take to integrate your data. 

Step 1:  Know what you have to work with.  That means you must identify and classify all data assets. Establish storage location, lineage, and access permissions. Quality third-party data discovery tools can help. A good tool will establish data lineage and ownership and implement access controls for each type. They will also establish compliance with HIPAA and GDPR regulations. 

Step 2:  Standardize and map all types of data. Your structured data assets will have predefined categories and relationships that facilitate storage, retrieval and analysis. Your unstructured data has no schema and can’t be stored in a database without transforming it first. What data transformations will you need? Two approaches are ETL (extract/transform/load) which transforms data on a processing server and ELT (extract/load/transform) which transforms data in the warehouse.  ETL is best used for structured data. ELT, on the other hand, works better for images, audio, and PDF or unstructured data. 

Step 3:  Examine your structured and unstructured data to determine whether you need a “data warehouse” or a “data lake.”  A data warehouse stores structured data after aggregating it from multiple sources and transforming it into a standardized format. Data intelligence tools draw from data warehouses to perform analytics. 

A data lake, on the other hand, stores data in its native format (PDF, audio, video). Data lakes support big data analytics, machine learning, and advanced analytics tools. Cloud data lakes are highly scalable and better for long-term data storage. If you are unleashing complex machine learning projects, you will need a data lake. 

Step 4:   If you’re bumping up against legacy software ERPs and old mainframe databases (often incompatible with modern cloud-native applications, IoT devices), turn to API software.

With a layer of application programming interface (API), you can separate and wrap legacy systems or even provide access to constituent web service operations. You can also rebuild the underlying application code.  

Application Programming Interfaces (APIs) offer a more cost-effective solution for creating new data integrations when compared to the expensive and time-consuming process of overhauling or replacing legacy systems. These interfaces provide a flexible and efficient means of connecting disparate systems without the need for extensive architectural changes. 

Sophisticated API management platforms enable healthcare organizations to efficiently handle data streams from modern sources such as medical wearables, connected hospital equipment, and remote patient monitoring solutions. You want your physicians to seamlessly incorporate data from cutting-edge technologies into their existing infrastructure, enhancing their ability to deliver comprehensive and technologically advanced patient care without the need for complete system replacements.

Step 5:   Build a secure data pipeline. Hammer out a number of steps for data ingestion, storage, processing, and access. You can even automate these steps. When moving data from one destination (maybe a PMS) to another (data warehouse, say), you don’t want data duplication. Reliable data pipelines can enrich data and give you a complete view of each patient. 

Step 6:   Establish data governance. You can derive your best insights from your data when you establish processes,  technologies, and policies that manage data availability, usability, security, and integrity. Of course, ethical and secure data use ensures you comply with applicable regulations. 

To ensure secure and efficient data management, organizations should deploy robust identity and Access Management (IAM) solutions. These systems automate the process of granting access to newly integrated data and datasets, ensuring that only authorized users can view or manipulate sensitive information. IAM solutions also allow for the implementation of fine-grained control over usage permissions, providing a nuanced approach to data access that aligns with organizational roles and responsibilities.

In addition to access control, it's crucial to implement comprehensive security measures to monitor both applications and infrastructure. These security solutions should provide continuous surveillance, threat detection, and rapid response capabilities to protect against potential breaches or unauthorized access attempts. By combining IAM with advanced security monitoring, organizations can create a robust defense system that safeguards sensitive healthcare data while maintaining the flexibility needed for effective data integration and utilization.

Of course, a data specialist, consultant, or third-party data company will carry out all of this work for you. Still, with this background, you can understand what you need and converse with any expert intelligently. 

Because payer contracts are a key source of vital data

In the coming years, it will be obvious that timely and accurate data makes a life-or-death difference for patients. It can make a viability difference for an MSO’s physician groups and practices, too. 

As the healthcare industry increasingly adopts data integration, forward-thinking MSOs will modernize medicine with interoperable, integrated PM systems, robust automation, AI, and revenue cycle technologies. 

Revenue cycle management (RCM) software supports and enhances PM system performance by automating several key tasks and improving the quality of the data in the PMS. Automated eligibility verification gets patients in faster. It integrates billing processes with the PMS, ensuring that services rendered are accurately billed. Via its analytics functions, it generates reports that ensure providers understand revenue patterns, identify bottlenecks, and optimize workflows. 

Proactive contract management and modeling facilitated by tools like MD Clarity’s RevFind support the provider by performing contract optimization. It ingests, processes and digitizes all contract data. Comparing each actual payer payment against terms listed in the contract and alerting staff to any discrepancies helps providers get their revenue. Payer underpayments are widespread. By addressing underpayments, organizations can increase recovered cash and improve profit margins. With RevFind, providers can also compare payer contract performance, a step that provides the data to negotiate better contract terms and fees. 

‍Schedule a demo to see how you take control of your contracts and sweep in the revenue guaranteed to you in your contracts. 

‍