Nowhere is our society’s dependence on data more evident than in the healthcare industry, where treatment, compensation, and research all depend on quality reporting. Due to the high cost of compliance, however, many healthcare facilities have failed to fully adopt clinical reporting standards. For medical practices to get to the point of being able to analyze their data effectively, practices must have an effective process for handling existing data. Although there is tremendous promise in the future for big data, most organizations need a data solution they can begin using today. These facilities need a system that collects, shares, and analyzes all data types from a host of different mediums.
Establishing a healthcare analytic framework begins the process of transforming any medical facility that doesn’t rely on data into a data-driven organization. One of the most popular models, developed by a group of veteran medical professionals with different backgrounds, is the Healthcare Analytics Adoption Model. This model gives healthcare facilities a comprehensive guide to adopting healthcare analytics and transforming the company’s culture into a data-driven organization.
Level 0-Fragmented Point Solutions
Most medical facilities begin at this stage of the model. They have inefficient and inconsistent analytic versions of the truth about data in their systems. In facilities at this level, fragmented point solutions are not co-located in a data warehouse or integrated with one another. At this level, creating reports for decision makers is challenging.
Level 1-Enterprise Data Warehouse
Medical facilities at Level 1 have started the process of developing a data-driven culture, but they have more work to do. Essentially, organizations at Level 1 have the foundation necessary to move forward, including a single data warehouse.
Level 2-Standardized Vocabulary and Patient Registries
Level 2 is where system organization begins. Practices at this stage have established a vocabulary, identified reference data, and they have standardized disparate source system content in their data warehouse.
Level 3-Automated Internal Reporting
At this level in healthcare analytics modeling, healthcare facilities are efficient and consistent with their data process. The system is fine-tuned, and the medical team can easily support basic management and business processes. Management can easily assess key performance indicators for more data throughout the day.
Level 4-Automated External Reporting
At Level 4, many organizations are beginning to reap the rewards of establishing a data-driven culture. During this stage, not only is the practice more efficient and consistent, but they are also able to respond with ease; they are agile.
Level 5-Waste and Care Variability Reduction
At Level 5, many organizations can leverage their systems to measure and manage evidence-based care effectively. These organizations focus on increasing outcomes by following clinical best practices, eliminating waste, and reducing variability. Additionally, these facilities are able to integrate population-based analytics to develop a better array of treatment options for patients.
Level 6-Population Health Management and Suggestive Analytics
With a focus on improving the quality of patient care, population management, and reducing costs, reaching Level 6 means organizations have moved beyond the walls of their facilities when it comes to being data-driving. These organizations have a focus on complete patient care, including, home monitoring, pharmacy data, and bedside devices. The systems in Level 6 organizations are updated quickly to maintain their heightened data-driven culture.
Level 7-Clinical Risk Intervention and Predictive Analytics
Level 7 facilities have broader motives than Level 6 facilities. These organizations are concerned with collaboration with other clinicians and payer partners. Medical facilities at this stage use a number of business tools to mitigate risks as they expand their analytics, including:
- Predictive modeling
- Support outreach
Level 8-Personalized Medicine and Prescriptive Analytics
At Level 8, health organizations are focused on providing patients with personalized care using data captured at the point of care and within health populations. At this stage, organizational analytics focus on intervention decision support, prescriptive analytics, and NLP text.
Through health care analytics your organization can easily identify what level you are in the Healthcare Analytics Adoption Model by reviewing how and what you use data for internally. If you determine that you are on a lower level, don’t be discouraged. The model should be worked as a process with the goal of inspiring medical facilities to obtain Level 8.
Understanding Data Quality Rules
Clinics and caregivers are hardly the only entities with an interest in health data. Federal and state benefit programs, private insurers, charities, and other groups regularly demand that healthcare providers adopt new standards for data recording and reporting. These standards are designed to make pertinent medical information more accessible to consumers, insurers, and technical and non-technical healthcare providers. The more consistent health data is, the easier it is for everyone involved to understand it and contribute to improved health outcomes.
Despite the manifold benefits of adopting consistent clinical quality standards, those standards place a serious burden on healthcare facilities. With rising costs and increasingly crowded hospitals, most providers have neither the resources to hire someone to update their data nor the time to do it themselves. Caregivers thus put updates off until long after the official deadline. Because many public and private benefits programs only compensate clinics that successfully adopt these standards, failure to comply makes it difficult for clinics to receive payment for their services, further straining their resources.
Even among hospitals and clinics that do comply with these reporting rules, there are often significant problems getting them to report the data accurately. The Meaningful Use e-measure program, for example, requires healthcare facilities and providers to prove that they are capable of reporting e-measures but not to resolve known quality problems or identify new ones. Many providers also fail to gather all of the relevant data or to store it all in the same databases, making even accurate records difficult to interpret.