A robust, cost-effective and future-ready IT infrastructure is critical for healthcare organizations as they try to increase efficiencies and lower cost of ownership in the face of increasing competition and shrinking margins. In the changing healthcare landscape, organizations must have the ability to capture, index, save, report, understand, track and interpret data. Besides reporting, data analysis presents a serious challenge as the industry steadily moves towards a value-centric model, breaking away from its fee-for-service tradition. While IT investments are on the rise, ROI and long-term objectives are rather blurred for many organizations.
Understanding the Challenges
Progressive IT solutions are being increasingly adopted on the clinical side of the healthcare industry. A slew of software applications, particularly in the EHR niche, have hit the market over the past few years. Some organizations are finding that they are changing internal processes in order to integrate new software solutions with the business. This tends to put additional stress on healthcare leaders and their staff. Even before ACA, exhaustive reporting requirements demanded a significant amount of IT resources. Reporting has become even more demanding with the reforms issuing stringent requirements. These requirements are fueling the search for better IT solutions. Furthermore, the October 2014deadline for implementingICD-10 coding standards is straining existing IT resources. Confusion is compounded by the different approaches in which data can be tracked and reported. Industry software vendors are presenting an array of solutions, making it difficult for organizations to choose which solution would be the best fit for their environment.
Data Analytics: Making Immediate Sense
The kind of savings that could be possible through detailed analytics is still being explored and all indications suggest that the scope is pretty high. For instance, regional blood pressure studies can show whether caregivers are prompting hypertension tests unnecessarily or utilizing for better care outcomes. It also provides caregivers a headstart when handling hypertension cases since detailed clinical studies drive better diagnoses. The use of data analytics for making financial decisions can be understood with a simple example—a health plan issuer can use analytics for quantifying and predicting the risk quotient across patient populations for the near future. Risk quotient calculations impact every aspect of a payer’s business decision, including strategies that directly impact profit margins.
The Biggest Challenge is Perhaps, Achieving Data Integration
Until now, healthcare leaders were content with data that could be contracted to metrics/ratios which clearly indicated profits/losses. With healthcare reforms, patient satisfaction and clinical performance have been added to the mix and has created the need for greater data integration. Care providers also need to integrate more clinical data to support better financial and clinical decision making. There is a greater realization that true business intelligence cannot be acquired until data from each department, specialty, resource, etc. in the organization is structured uniformly. However, creating a common data structure isn’t easy. Over the years, different IT trends have prevailed. Some of these were addressed with permanent solutions, i.e. software solutions bought and installed with an endless lifecycle. This has created a problem. Collating information from disparate systems makes data warehousing and indexing time-consuming and expensive.
Role of Governance
Adopting IT solutions to address the challenges discussed above clearly indicates the need for visionary planning. This is particularly applicable today when most organizations find their budgets limited and comprehensive IT overhauls impractical. It becomes important to adopt solutions that can take care of the most pressing requirements first, such as reporting for compliance. However, some spending should be allocated toward acquiring greater business intelligence. This means spending on future-focused applications that allow organizations to create information systems where tools like analytics can complement better decision making. This essentially means creating a hybrid IT model where prioritization is combined with immediate profitability and long-term planning.
Reaching a Common Ground
There has been a continuous shift toward providing end users with more access to data. Dashboards are becoming a common way to display data in a safe and user-friendly way. However, data for end users has to work promptly and smartly so that users can handle it independently. Thus, data warehousing, data integration and interoperability are likely to become common investment zones.
Do you agree with our perspective? Share the challenges you have faced when planning your organization’s data-related strategies.