Clinical data has been leveraged for many years by health and pharmaceutical researchers looking for the new treatments to improve patient care or address specific medical conditions. As healthcare has changed, however, the importance of clinical data and management of the data has grown.
The advent of value-based healthcare along with a continuing focus on improvement of processes that contribute to enhanced patient experience and improved outcomes, has expanded the use of clinical data to augment administrative and financial data. The combination of different types of data supports a holistic view of a healthcare organization’s operations – resulting in more effective quality improvement efforts.
Because the use of clinical data is no longer the straightforward use for research that is focused on one clinical trial, and its use goes beyond a simple review of a medical history to inform care for an individual patient, clinical data management (CDM) professionals face a myriad of challenges.
Clinical data management challenge #1
The first challenge is the sheer volume of data collected by healthcare organizations. While electronic health records (EHRs) are responsible for the greatest growth in data in recent years, other data collection tools for administrative tasks such as staffing, financial activities such as claims submissions and inventory systems for medications, supplies and equipment, have grown exponentially.
According to a 2014 HIMSS Analytics Report, 90 percent of survey respondents representing hospitals with fewer than 150 beds said they support up to 100 applications within their organization, and half of respondents representing hospitals with 500 or more beds said they support more than 250 applications. Integration of information from all clinical, financial, administrative and operation systems enables process improvement staff to evaluate opportunities to enhance care, reduce wasted time and supplies, and better manage costs.
In addition to clinical research and clinical or process improvements, the combination of clinical and other data are studied together to develop population health management programs, create new healthcare services and streamline the generation of value-based reports for regulatory agencies and accountable care organizations (ACO).
Clinical data management challenge #2
The second challenge faced by clinical data managers is the number of disparate systems – even within the same area of the hospital or healthcare organization. These silos of information are collected, managed, accessed and used by a variety of people who may unknowingly be duplicating efforts of other departments. Expanded use of integrated data means that a wide range of people across the organization need access to the information for many different reasons.
Overcoming the silo challenge requires a combination of hospital organization leadership along with technology. Top leadership involvement in the redesign of data management activities increases the likelihood that different departments will work together to share governance of their data – working with a CDM team to ensure the plan addresses the concerns and needs of all entities within the organization.
Clinical data management best practices
While clinical data management services is a complex, ever-changing field, there are a number of basic best practices that can ensure a health organization’s data collection and data validation for clinical trials, transferred data between EHRs and other systems, and integration of information from different systems produces accurate quality data as well as clinical study data.
Three clinical data management best practices for healthcare organizations are:
- Identify your resources – data, technology and people
Because data is collected and stored in different systems, and because each system may have its own “data managers” and “analysts,” it is important to locate all data and identify people or departments responsible for the data. In addition to identifying people and type of data collected by the department, it is also important to produce a list of all technology – software and hardware – as well as processes used to collect, store, analyze and share data. Remember to include any paper-based data collection methods that relies on staff-based data entry as well as electronic data capture tools to ensure the inventory of data management processes and tools is complete.Using this information, develop a clinical data management team or if more acceptable to non-clinical members of the team – a data management team. Working together the team members can identify how data is used – for process or quality improvement, financial reports, cost reports to insurers or Medicare, patient satisfaction studies, or clinical trials within the organization and with third parties outside the healthcare organization.Not surprisingly, this exercise often results in the realization that some data collection efforts duplicate the activities of other departments.
- Develop data management plan
Although projects for quality improvement, clinical studies or other administrative audits will require individual plans that specify problem to be studied, data required, format used and archive or storage needs, it is also important to create an overall data management plan.An organizational data management plan should not only delineate who “owns” or is responsible for the governance of the data, but also who is responsible for approving access to shared data. HIPAA and HITECH privacy and security requirements not only make it essential to limit access to clinicians or staff with an appropriate need to access the EHR or other information, but ongoing review of who is authorized to access data for specific purposes provides an opportunity to keep access controls up to date and valid.A clinical data management plan should also address storage of data. Authors of the 2014 HIMSS Analytics Report found that the majority of healthcare providers treat all data as active and define it as “stored onsite for immediate access.” However, survey respondents also reported that data is less likely to be accessed over time. In fact, by year three, only 22 percent of data was accessed and about 20 percent of data generated was never accessed at all – statistics that applied to clinical, operational and laboratory data.
To address the reality that technology will continue to change, data collection will continue to grow, and not all data will be needed forever, the CDM team can establish parameters that define how long and what data will be stored for active access, and when data will be archived – still accessible, but not in real time. This is a critical step to improved, cost-effective management of data.
- Create platform to connect disparate data silos
Although it makes sense that quality improvement staff should be able to access data from clinical and administrative systems, the reality is that data management systems located in different departments or sites within an organization do not always “talk the same language.”With some systems relying on structured data and other incorporating unstructured data into their records, as well as applications and systems with limited functionality, enabling interoperability among the disparate systems can be financially and logistically out of reach for some healthcare organizations.One solution is the use of a platform that runs “under” the myriad of applications and systems currently in use. By relying on one technology to connect, aggregate, integrate and harmonize data so multiple users can access it easily to establish patient registries, clinical data repositories, or ACO data harmonization. Serving as a big data repository, the platform can provide on-demand, self-service access to clean, quality data that can be used to analyze and inform best practice development, operational changes, clinical trial reports, administrative activities or financial operations.
Listing only three best practices does not mean that creating a clinical data management system that combines clinical, financial and business data to create operational processes that optimize use of staff and technology resources is easy. It is, however, possible with a clearly defined strategy, support from organizational leadership, multi-disciplinary collaboration and reliance on third-party support to address the technology and staffing needs to resolve interoperability and integration issues.
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