Five Pitfalls Post M&A Data Integration | Whitepaper, BiaaS Solutions

Consolidation in the healthcare industry has been alive and well for over a decade, and mergers and acquisitions (M&A) in this space continue to be very active. Hospital systems, private equity (PE)-backed management service organizations (MSOs), and growth-oriented groups acquire or contract for controlling interest in smaller provider practices. Often, these deals involve cash up front and performance-based milestone payments.

It’s a win-win, enabling owner physicians to focus on providing clinical leadership and serving patients while the management company handles functions like HR, IT, purchasing, accounting, revenue cycle management (RCM), credentialing, payer enrollment and contracting, and compliance. These consolidations aim to increase operational performance, boost profitability, and improve patient outcomes through centralization, standardization, and leveraging economies of scale.

Month-end reporting for the board of directors
Revenue cycle reporting that demonstrates how well the business is being paid for the work performed
Operational reporting on the performance of each location/provider/business line, usually in terms of volume
Clinician reporting that helps foster trust by providing physicians with full transparency, especially in the numbers that drive provider compensation
Acquisition partnership reporting that substantiates milestone payments for physician owners and clearly delineates between pre- and post-acquisition timelines
PE-sponsor transition reporting, providing timely and well-organized data to potential new sponsors to build confidence and enthusiasm

Where Traditional Integration Solutions Fall Short

Many M&A integration projects involve migrating the newly acquired locations onto a centralized electronic medical record (EMR) or practice management (PM) system. With the various acquisitions inevitably using disparate EMR/PM systems, this process can take months. Meanwhile, leadership is eager to gain insight into the new acquisition’s performance and is keenly aware of the need to show continued improvement, requiring year-over-year (YoY) reporting that spans the old and new systems.

Though many EMR/PM systems use standard formats for importing clinical data, to date almost none will support migration of historical appointments and financials between systems in a way that allows for immediate turn-down of the legacy systems. That’s a significant gap, often forcing companies to keep using the old systems for some time, such as for working down accounts receivable.

Producing timely financials at the end of each month is a crucial requirement, and one that becomes more challenging post-acquisition. With each acquisition potentially adding another system to pull reports from in order to close the month, it takes more time and effort to accomplish this foundational task.

Manually extracting data (often viewed as the only feasible approach) chews up significant team resources each month. It also results in a very limited set of reports that don’t fully meet the organization’s needs for accurate, complete information.
Creating a data warehouse internally is time-consuming and complex, requiring substantial IT and business SME resources. Many IT teams struggle to find time for this significant project amidst competing priorities. A project of this nature demands that IT resources understand the business needs intimately enough to fulfill them with a homegrown solution, a challenge that often leads to protracted projects yielding data of questionable reliability and limited utility. When the team experiences turnover (not unlikely in today’s labor market), institutional knowledge is lost and the project takes a big step backward. And depending on the IT team’s skillsets and experience, the resulting solution might be complicated to use and inefficient to maintain.
Hiring consultants to build a custom data warehouse usually comes with a premium price tag and takes years to accomplish. Meanwhile, the organization resorts to manually extracting and organizing data, which is time-consuming and unwieldy. Perhaps the biggest risk is inadvertently hiring a consultant that doesn’t understand the business. Given the significant time and energy required to get developers up to speed on the nuances required to model the business properly to deliver accurate, reliable performance data, this approach requires more investment than is apparent at first glance.

1. The Pitfalls of Extracting Data from Source Systems

Because it’s labor-intensive to log into multiple source systems and pull the requisite data, manual report extracts tend to happen less often than the business needs actionable data, usually only monthly. Many systems restrict the amount of data you can extract at one time, resulting in a need to pull an ever-expanding series of reports just to cover the desired timeframe.

For example, some EMR/PM systems only allow you to cover a three-month timeframe in detailed reports; others run forever when you try to extract too much data. Pulling just one year’s worth of data could require running the report many times.

Worse, companies find it challenging (or impossible) to access all the data they need for a comprehensive picture of the business.

For instance, most source systems require pulling a large variety of reports to gain a complete picture of:

Patent and provider demographics
AR balances
Charges, adjustments, and payments
Balance responsibility transfers (between patient, primary, or secondary, for example)
Appointments
Locations
Insurance companies
Referral sources

In most cases the reports available from a source system only provide data from today’s perspective. By hiding changes and deletions that occur over time, they make it tough (even impossible) to reproduce historical reports accurately. Often, it’s not feasible to view report data that’s vital for analyzing utilization and availability, such as provider schedules and open slots.

Manual data extraction also wastes precious resources, consuming time that accountants and analysts could spend on value-added activities. As a result, it’s common to miss opportunities or to fail to timely identify and resolve problems.

Additionally, data extraction creates a host of technical obstacles for the IT team or consultant building a custom data warehouse. Records processing can be inefficient, bad data files require fixing, and source system vendors might deliver data too late to include in a daily processing run.

2. The Pitfalls of Merging Data for Reports Pitfalls of Extracting Data from Source Systems

Given that each source system stores and reports on data differently, it can be challenging to organize it in a way that enables reporting on key factors across the organization. It often takes significant effort to understand each system’s nuances well enough to represent the data properly. This slows the integration process and hampers the ability to access information that’s critical to running the business.

For example, each acquisition’s system likely uses different names to represent the same insurance companies, plans, and appointment types. Creating a reporting platform that cleanly spans all the source systems requires managing the mappings to align different entries under clear reporting categories. It’s a data stewardship task that needs constant effort.

System migrations add another wrinkle to merging data for reporting. When new appointments are imported from the old system, they all appear to have been scheduled on the import date. That can break analytics which look at how far out patients are booking appointments. Likewise, a means of linking patient records from the old to the new system is important for continuity of key metrics, such as duration of care and first/last visit timing, or simply to count the number of patients without duplicates. Further, achieving seamless reporting across the continuum of time spanning the migration requires mapping pre- migration providers and locations to their post-migration counterparts.

Human error creates its own set of data integration pitfalls. For instance, many teams use Microsoft Excel to merge data, requiring separate workbooks for each system which are then linked together. But it only takes one bad cell reference in one of the hundreds of formulas to result in incorrect numbers, calling the data’s quality into question and jeopardizing the analysis.

3. The Pitfalls of Silos & Inconsistency

Every healthcare company understands that it takes complete, accurate data to run the business day-to-day and to scale it efficiently and profitably. Yet traditional methods for integrating data post-M&A tend to result in siloed and inconsistent data use.

For example, it’s common for each department or function to pull data from source systems independently. Finance is mostly interested in closing the month and reconciling, RCM needs to see claims and collections, and operations needs to see daily appointments. This can result in each area working from a different “version of the truth.” It’s not uncommon for groups to only trust the data THEY produced and for the data of various groups to tell slightly different stories.

Likewise, different functions often pull source reports into their own Excel models, which are rarely consistent across the organization. That makes it very difficult to use the data to perform company-wide analyses, spot trends, or identify improvement opportunities.

Traditional approaches to integrating data post-M&A are likely to result in inconsistent reporting across the company. And that can quickly erode trust with the partner owners and the board.

Let’s say the finance and RCM teams believe monthly data extracts are sufficient for their needs. On the other hand, operations needs data daily, but the organization lacks the time and resources to pull and organize it that often.

Inevitably, some operations groups will solve this problem proactively by starting to track their own metrics via manual data entry. Yet, it’s highly unlikely those home-grown metrics borne from manually tracked data will align with the measures from source system reports the rest of the organization is using.

Since manual data entry and Excel formulas are error-prone and few organizations can devote significant time to checking validity, these manually generated reports are often riddled with errors that are tough to spot. Unfortunately, they tend to emerge in the midst of heated management disputes or worse, in litigation.

Inconsistent data can even begin as an issue before an acquisition deal closes. During due diligence, the data reported on business performance directly impacts deal valuation and payment milestone definition. Without a solid data model, there’s a real danger that pre- deal figures don’t align with the numbers produced by the ongoing financial reporting done post-deal.

4. The Pitfalls of Limited Data Sophistication

The extent to which an organization can be data driven depends greatly on the sophistication of its data models. In the early stages, most organizations use simpler data models just to measure volume (such as visits and collections), leaving much of the effort to improve the business to the “art of management”. As a result, they lack the substantially more actionable insights that can be gleaned with centralization and the use of more advanced methods to organize the data, gain agility, and surgically achieve success.

The reality is, decentralized data models don’t often produce advanced metrics, nor do they support a variety of metrics from different users’ perspectives. For example, leadership often finds usefulness in ratios like patient visits per calendar workday or per provider workday, because they smooth over months with different numbers of workdays. On the other hand, clinicians often tend to prefer the raw visit counts.

As companies migrate acquisitions onto central EMR/PM systems, they also need to avoid duplicating patient counts. Yet most integration approaches don’t merge duplicate patient accounts into one patient “person”, making it difficult to perform accurate patient population analyses.

Healthcare organizations also tend to lack access to data points that leverage more advanced processing. A prime example is the ability to estimate revenue, future volume, and schedule utilization more accurately, which requires visibility into provider schedules and open slots. These are often not available from source system reports, and the data are likely too sophisticated to be available in traditional approaches.

5. The Pitfalls of Limited Scope

EMR/PM systems are painfully limited in scope, typically only providing tools for scheduling appointments, documenting visits, and managing claims. Anyone who has worked in medical or dental practice management knows that there’s much more to running the business than these systems can support on their own.

Growing practices rely on a collection of other ancillary systems, including call center platforms, marketing/sales platforms, customer satisfaction surveys, inventory management (which some EMR/PM systems provide, but with much more limited functionality), IT ticket systems, HR systems, payroll/time-and-attendance systems, and other mechanisms for tracking processes.

These additional systems add layers of complexity to the post-M&A data integration effort. Just like with EMR/PM systems, it takes time to migrate each acquired entity onto a single set of centralized systems. During that time, the business needs to report across the set of systems for a broad picture of business performance.

Yet, without a solid data infrastructure, these ancillary systems rarely (if ever) are integrated into the reporting environment. That leaves loose ends, multiplies the effort to obtain a complete picture of the business, and severely limits leadership’s ability to gain insights and make improvements to unite and propel the business.

A Better Model for Post-Merger Data Integration

Finance, operations, and technology professionals in growing healthcare businesses grapple with these obstacles daily. What few realize is that there’s a more effective model for integrating data post-M&A that overcomes common hurdles and ensures healthcare businesses have the robust, complete, accurate, and timely data their success depends on.

By leveraging a data warehouse solution that’s purpose-built for healthcare, businesses can leapfrog the delays of custom development, avoid the high cost of creating a solution from scratch, and eliminate the inefficiencies and errors of manual extracts—all while putting much more actionable data at the fingertips of everyone who needs it.

Data warehouses designed for growing healthcare organizations can automatically draw data out of each EMR/PM system and ancillary system daily, organizing it for reporting in ways that help companies create a data-driven culture and achieve their ambitious performance goals. These solutions can provide access to the most common data types, like appointments, financial transactions, patient visits, provider workdays, and many more. Additionally, advanced data warehouse solutions can offer more robust, sophisticated data that’s vital to running an effective and profitable operation, including revenue estimation and schedule utilization.

It’s important to understand what to look for in a data warehouse solution uniquely designed to support post-M&A integration. The following are just a few examples of how a data warehouse pre-built for healthcare businesses can deliver the information needed to drive high performance and improve ROI.

Actionable Insights. Leveraging the solid base that a healthcare-specific data warehouse provides, companies can take advantage of the model to deliver a wide range of metrics and trends to each team member. For instance, a healthcare-oriented data warehouse allows organizations to use personal performance dashboards to drive team members toward specific metric improvements, often linked to performance-based bonuses.
Provider Workdays & Provider PTO. Lack of provider availability is typically a chief reason that a practice lags in performance. With accurate data on past and future provider workdays, and inversely provider PTO, operational leaders gain the insight to address staffing challenges before they impact the bottom line.
Revenue Metrics. Companies need to analyze revenue performance from many angles, which is something a healthcare-specific data warehouse can support. For instance, by accurately estimating revenue for charges and appointments, the practice can book revenue quickly, compare actuals against budget, adjust revenue forecasts, and fine- tune the estimation model. Through a centralized data warehouse, the organization can begin to create consistent, advanced metrics like these, which everyone in the business can share.

Attrition Risk by Patient. For specialists who see patients on a regular cadence, projecting revenue accurately demands knowing the attrition risk. With access to the right data, it’s possible to analyze whether patients have future appointments scheduled, the time since their last appointment, and how that timeframe compares to their typical frequency.

These insights help determine if the patient is at high, medium, or low risk of attrition. By using a well-constructed data model as a base for integrating machine learning algorithms that help predict no-shows, healthcare businesses also can make more informed decisions about overbooking.
Migration Smoothing. As acquisitions are migrated from legacy EMR/PM systems onto centralized systems, a strong healthcare-specific data warehouse will ensure that imported appointments and financials are appropriately pruned and mapped. This approach enables smooth reporting across the continuum of pre- and post-migration data.
Patient Person Identification. To report correct figures on patient populations post- integration, a data warehouse purpose-built for healthcare can automatically group together charts for the same patient, avoiding duplicate patient counts.
Schedule Utilization. Reporting on scheduled/seen appointments is a basic requirement that’s easy to achieve, but it doesn’t provide direct insight into schedule utilization.

A more advanced deliverable of a healthcare data warehouse solution is to model open slots as a means of expressing schedule utilization, so leadership can properly align staffing (supply) with marketing efforts (demand). This should include overbooking expectations, schedule blocks, operating hours, holidays, physical capacity (e.g., exam rooms, dental chairs), and insight into provider PTO.

HealthBiaas: The Healthcare Business Data Solution

Today’s healthcare businesses can’t afford to operate without easy access to reliable, complete, accurate data. It’s critical to running a thriving business and scaling it for high growth. That’s why medical and dental management teams are turning to HealthBiaaS from BiaaS Solutions: an existing data warehouse solution purpose-built for growth-oriented practices.

HealthBiaaS was developed by seasoned veterans of the healthcare consolidation industry, based on more than a decade of experience developing and enhancing healthcare-specific data warehouse solutions. This solution helps growing practices achieve ambitious performance goals and scale profitably, by facilitating efficient data integration and providing the data warehouse features and functionality these businesses demand. For example:

It automatically draws data from each EMR/PM and ancillary system, loads it into a staging database, transforms it to conform to the HealthBiaaS data model, and organizes it for effective reporting.
All the data facts and dimensions are already tailored to the healthcare business model, yet customizable to each organization’s needs. For example, pre- and post-acquisition data is clearly delineated for easy reporting.
It’s entirely based on standard Microsoft SQL Server technologies that can be hosted in the cloud or on local servers, with no hidden or compiled code. That makes it extremely easy for IT teams to work with.
It enables staff to consume practice performance data through tools they already know and use. For instance, analysts can use tools like Microsoft Excel and PowerBI to explore and analyze data in greater depth, while any user can easily run and subscribe for automated distribution of canned reports using SQL Server Reporting Services (SSRS). The data can easily be consumed by any industry-standard dashboarding or financial planning/analysis product.
Since the code is wide open for reading and editing, the solution enables internal IT developers to leap-frog the learning curve and start working with a mature base of code they can continue to evolve to fit the business’s changing needs.

HealthBiaaS streamlines the data integration process, with the ability to integrate to nearly any third-party EMR/PM system. For instance, we have ready-to-install integrations for leading EMR/PM systems such as:

Modernizing Medicine
AthenaONE, by athenahealth
Nextech
Dentrix
QDW

In addition, prior to forming BiaaS Solutions our team created integrations with a variety of other healthcare systems, affording us the intimate knowledge and experience to get such integrations up and running fast. These include:

AthenaPractice, formerly GE Centricity
eClinicalWorks (eCW)
NextGen Office, formerly MediTouch/HealthFusion
Intelligent Medical Software (IMS)

Given our team's extensive experience integrating EMR/PM systems, most other systems can be integrated relatively quickly. HealthBiaaS is the solution to the data challenges today’s medical services organizations face post-M&A—delivering the complete, reliable, actionable information that healthcare businesses need to scale and thrive.

Read our AQUA Dermatology case study to learn how HealthBiaaS helped a rapidly growing MSO gain a complete picture of how the business is performing across critical KPIs. Contact us today to schedule an introductory call.

WHITEPAPER

Five Pitfalls of Post-M&A Data Integration for Growing Healthcare Businesses

Where Traditional Integration Solutions Fall Short

Five Pitfalls to Avoid in the Post-M&A Data Integration Process

A Better Model for Post-Merger Data Integration

HealthBiaas: The Healthcare Business Data Solution

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