Electronic Health Reporter

Guest post by Mark Myers, Datalink.

Today’s healthcare IT departments have a relatively tall order when it comes to effective EHR data management. In an environment that often requires them to be simultaneously budget-conscious, growth-minded and patient-driven, healthcare IT must also address the often-competing data management needs for:

• Access
  • Instant
  • Secure
  • Anytime, anywhere
• Availability
• Security
  • Data sharing
  • Data at rest
• Privacy
• Compliance
  • HIPAA
  • PPACA
  • CFR
• Data protection
  • Backups
  • Operational recovery
  • Disaster recovery
• Capacity planning
  • Archival
  • Growth
• Data mining and analytics

Popular EHR system vendors have made significant strides to address several of these data management issues. Unfortunately, they can only go so far given the current state of many healthcare IT environments. Some departments may still require custom software applications, complete with specially configured servers, storage and network hardware to support them.

Likewise, many health IT data centers lack a technology refresh program and continue to use inefficient legacy applications, unsupported versions and aging hardware, which can lead to low resource utilization, issues with data availability and high on-going IT support costs. Each of these examples can present a roadblock to cost-efficient IT services, streamlined data access, data sharing or centralized data management surrounding backup, DR or long-term digital archival.

To achieve streamlined-yet-comprehensive data management that meets the needs captured in the above list, a broader approach is required. Service improvement is not just a matter of deploying updated software or just upgrading hardware. Based on our work with healthcare IT organizations across the U.S., we find it often takes both a “top-down” and “bottom-up” approach to data management.

The “Top-Down” (Strategic) View of Healthcare Data Management

In many IT environments, including healthcare, much of our on-going analysis and assessment work involves first understanding the usage and demand needs of the organization’s data itself. Only then can we focus fully on the best-fitting technology or architecture to meet those needs.

More comprehensive than the typical list of EHR data characteristics, these inquiries relate to the overall lifecycle of an organization’s data and how it should be addressed at each stage. Although related, Picture Archiving Communication Systems (PACS) data has different needs than just technician or doctor notes regarding the last two patient visits. The types of data about their visits accessible by patients from their own, potential digital “portal” will differ from what’s available to Patient Billing departments or a floating Certified Nursing Assistant (CNA) assigned to patient care. A host of questions need to be asked and answered. A short sampling includes:

• Where does the data need to be shared outside of the hospital system and when?
• How much of it should be shared?
• Where do you need to ensure security safeguards?
• How often will the data be accessed and by whom?
• How many concurrent users will there be of the system housing the data?
• How long do you want to wait to access the data?
• Which types of records require immediate access vs. a somewhat longer delay (i.e., older records accessed less frequently)?
• What privacy or compliance rules govern the hosting and sharing of the data?

Once we have a clearer picture of the data’s characteristics and needs over its lifecycle, we can begin defining overall data management policies for access, sharing, storage, and archive (some automated, some manual) that fulfill this criteria.

The “Bottom-Up” (Tactical) View of Healthcare Data Management

First armed with the “Top-Down” view of a healthcare organization’s data management needs and key policies to address those needs, healthcare IT can then take a successful “Bottom-Up” view. Here, the focus turns to current IT technologies and architectures and how well they meet the data management needs and policies already defined. Identifying high-priority gaps or obvious areas of frustration, cost and patient complaint can lead to a useful look at suggested updates or replacements to the current IT infrastructure. Other areas may focus on where existing IT systems seem unable to meet current service requirements.

In general, we find many such issues are often addressed through transformational changes that involve one of more of the following:

• Consolidation of legacy IT hardware server and storage silos onto a centralized platform

• Establishment of a virtual data center (VDC). Creation of a VDC means extending virtualization past servers alone to many other areas of the data center.

A VDC can include virtual servers, virtual storage and virtual networks. It may also include virtual desktop infrastructure (VDI) which supports highly distributed environments, such as remote offices. VDI addresses data security issues by keeping all data within the data center walls. The user then only sees a display of the data.

• Implementing unified computing (UC) architectures which involve pretested, vendor-preapproved building “blocks” of servers, storage and networks. Such pretested blocks offer integrated functions associated with automated data movement and automated data management.

•  In some cases, this may also mean the ultimate transformation of the healthcare IT environment to embrace an on-demand, private cloud construct, public cloud services or a hybrid mix between the two.

The Successful Outcome of Top-Down/Bottom-Up Data Management

Once you understand the nature of the data itself and have begun to implement automated policies on top of the right technology architecture, the results for healthcare organizations can be rapid and often quite remarkable. In one case, this type of analysis and implementation led a prominent public healthcare system in the Midwest to embark on its own transformation journey. Its ultimate goal is delivering a private cloud to its patients, doctors, hospitals and clinics.

As part of its efforts, the healthcare organization modernized IT operations, streamlined its data backup and recovery and implemented a unified, virtual data center to further consolidate, manage and share data. Thus far, these moves have contributed to significant reductions in patient wait times and the ability to provide more innovative data services. The organization has also improved doctor access to patient information. Over the next few years, the organization anticipates achieving a 200% ROI on its initial IT investment.

Effective data management in the new healthcare economy can be a daunting task, but not impossible. As the above example demonstrates, using the right data management strategy and the right underlying technologies can bring great reward to both patients and their caregivers.

Mark Myers leads Datalink’s Advanced Services Cloud Service Management practice helping Fortune 500 and mid-sized organizations transform for better alignment between IT and business units to drive greater IT efficiency and improved use of technology for IT services.