Obtaining a Data-Driven Culture in Health Care Analytics

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freedigitalphotos.net/pong

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 health care 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 health care facilities a comprehensive guide to adopting health care analytics and transforming the company’s culture into a data-driven organization.

Level 0-Fragmented Point Solutions

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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

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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

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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

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At this level in health care analytics modeling, health care 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

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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

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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

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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

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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
  • Forecasting
  • Triage
  • Escalation
  • Referrals
  • Support outreach

Level 8-Personalized Medicine and Prescriptive Analytics

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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 on 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.