The Evolution of Healthcare Information Systems

First, I want to review the history of healthcare information systems and how analytics came to be so important. I’ve been in the industry long enough to see many of these changes occur, and I believe that a data warehouse and analytics tools are core components of any CIO’s application portfolio. This might make more sense with some historical perspective.

Let’s review health information system trends, decade by decade. I’ll list the main influence driving healthcare, the driver for IT, and the resulting health information technology (HIT) innovation:

1960s: The main healthcare drivers in this era were Medicare and Medicaid. The IT drivers were expensive mainframes and storage. Because computers and storage were so large and expensive, hospitals typically shared a mainframe. The principal applications emerging in this environment were shared hospital accounting systems.

1970s: One of the main healthcare drivers in this era was the need to do a better job communicating between departments (ADT, order communications, and results review) and the need for discrete departmental systems (e.g., clinical lab, pharmacy). Computers were now small enough to be installed in a single department without environmental controls. As a result, departmental systems proliferated. Unfortunately, these transactional systems, embedded in individual departments, were typically islands unto themselves.

1980s: Healthcare drivers were heavily tied to DRGs and reimbursement. For the first time, hospitals needed to pull significant information from both clinical and financial systems in order to be reimbursed. At the same time, personal computers, widespread, non-traditional software applications, and networking solutions entered the market. As a result, hospitals began integrating applications so financial and clinical systems could talk to each other in a limited way.

1990s: In this decade, competition and consolidation drove healthcare, along with the need to integrate hospitals, providers, and managed care. From an IT perspective, hospitals now had access to broad, distributed computing systems and robust networks. Therefore, we created integrated delivery network (IDN)-like integration, including the impetus to integrate data and reporting.

2000s: The main healthcare drivers were increased integration and the beginnings of outcomes-based reimbursement. We now had enough technology and bedside clinical applications installed to make a serious run at commercial, real-time clinical decision support.


Healthcare Drivers

IT Drivers

Resulting HIT

  • Medicare/Medicaid
  • Expensive mainframes
  • Expensive storage
Shared hospital accounting systems
  • Hospital-wide communications (ADT, OC, Bed Control)
  • Broadened administrative systems
  • Departmental systems processing
  • Smaller computers
  • Improved terminals and connectivity
  • Expanded financial and administrative systems (PA, GA, HR, MM,OP/POB)
  • Results review
  • Selected clinical department automation (Lab, MR,RX)
  • DRGs
  • Networking
  • Personal computers
  • Cheaper storage
  • Independent software applications
  • Integrated financial and clinical (limited) systems
  • Managed care
  • Financial and administrative systems
  • Departmental imaging (limited systems)
  • Competition
  • Consolidation
  • Integrated hospital, provider, and managed care offering
  • Broadened distributed computers
  • Cheaper hardware and storage
  • Expanded clinical departmental solutions
  • Increased IDN-like integration
  • Emergence of integrated EMR offerings
  • Increased integration
  • Beginnings of outcomes-based reimbursement
  • More of everything
  • Mobility
  • Emerging cloud computers and cloud based big data analytics
  • Emerging, broad-based clinical decision support
  • Broad operational departmental systems with EMR integration
  • Emerging data warehousing and analytics solutions

A Fortuitous Byproduct of Healthcare IT Implementation

As the decades passed, the most commonly implemented systems were those designed to automate transactions, either in a clinical or administrative context. An obvious result of more transaction systems installations was the dramatic increase in readily available digitized data. I like to think of this data as pure exhaust from transaction systems; we certainly didn’t install the systems for the data, but data emerged as a critically useful byproduct. All of a sudden, we found ourselves with enormous amounts of data siloed in multiple, discrete applications. Pioneers, such as Dr. Brent James at Intermountain Healthcare, began to articulate to the industry that improving operational performance would require health systems to merge and analyze this data.

Another focus of hospital information system implementation over the years has been reporting. Reporting systems typically exist as components of transactions systems. Historically, this reporting provided snapshots of information about the hospital to management, the board, or other groups.

As valuable as these reporting systems were, they can’t meet today’s industry analytics requirements. Today’s focus, out of absolute necessity, must be on performance improvement; especially on the clinical side. Essential to this focus is the need for an analytics offering that bridges and merges multiple applications: clinical systems, financial systems, and patient satisfaction systems. Reporting systems embedded in a transaction system clearly can’t do that. Furthermore, analytics requires more than mere reporting; health systems must support the ability to drill down into this comprehensive, merged data to achieve real insight into operational performance. Finally, complex analytics queries against millions of rows of data cannot be performed on transaction system databases without adversely affecting performance; a separate data warehouse is required.

What CIOs Need Today

Now we find ourselves in the 2010s. The healthcare drivers are accountable care organizations (ACOs) and other value-based purchasing initiatives, a need for cost and quality-control systems, and a broadening genomic influence on personal care. Our main IT driver is pervasive computing. We have microprocessors everywhere. We’ll be seeing more and more of them, along with the proliferation of data. IT in the industry has broadly implemented EMRs and operational data systems, and, ultimately, these EMRs will have pervasive clinical decision support. Knowledge gained from analyzing an organization’s data in search of performance improvement insights will complete the operational systems cycle by refining the rules essential for successful clinical decision support. These efforts are highly complementary.

To handle all of this data, and achieve cost and quality benchmarks, CIOs must implement an enterprise analytics solution. In fact, I firmly believe a healthcare organization’s primary expectations from IT will be:

  • Supporting and enhancing reliable operational systems
  • Offering comprehensive access to needed information via an agile data warehousing and analytics offering

Yes, transaction systems must be implemented and run reliably. But this will become, over time, more of a maintenance function than strategic imperative. Information now drives strategic innovation in the health system, and actionable information comes from an effective data warehousing and analytics solution.

Today’s CIOs are working extremely hard to fulfill their marching orders to install or replace enterprise EMRs and other important transaction systems. In spite of this, they must turn their attention to data warehousing soon; if not now. I feel confident predicting that if analytics isn’t on your CEO’s mind now, then it will be very soon. Organizational leadership can’t ignore the many performance improvement successes resulting from effective analytics applications.

In summary, for CIOs to retain their strategic value as chief information officers, they must be actively engaged in a strategy that results in the capture and analysis of comprehensive data, which enables the health system to become an organization that is constantly improving and learning.