About Data Analytics

Five Core Components of Analytics 

Until recently, the ability to quickly analyze and extract results from large quantities of data was unavailable to many organizations. Rapid advances in computer hardware and software have brought theses sophisticated techniques well within the range of smaller enterprises. These capabilities have allowed these organizations to “compete on analytics”, meaning that they make their ability to rapidly analyze data and act on the results a key competitive strategy.

The full scope of analytics includes deployment of all or a mix of the five core components.

  1. Extract and Join Data
    Data from large, diverse data sets often use different “key” values as identifiers. For example, medical claims generally identify patients by a patient identifier, and physicians by a physician ID. However, drug claims often use a medical records number as a patient identifier, and a BNDD number for physicians. “Lookups” must be created to allow for combined analyses of differing data sets; for example to evaluate the prescribing patterns of drugs (from the drug data) for specific diagnoses (from the claims data). The process may also involve using one data set to specify conditions for another data set. For example, clinical abstracting data may be used to identify Emergency Department patients, whose services will be summarized from claims data.
  2. Develop Data-Based Models
    Models are developed using data sets developed above as inputs. For example, these models may be used to identify diabetic patients from a combination of medical claims (to identify certain diagnoses) and drug claims (to identify patients taking drugs used to treat diabetes). Other models determine the hierarchal condition categories (HCCs) of patients based on multiple diagnoses contained in claims data. Still others will be used in the formation of ACOs to associate patients with primary care physicians. Third-party payments can be modeled from combining claims or billing data with payment methods and rates.
  3. Develop Metrics and Key Performance Indicators (KPIs)
    To be useful, metrics must be well-defined, consistently measurable from accurate information (e.g., data models), and linked to the goals and strategies of the organization. KPIs create context for metrics by identifying expected value ranges for performance. They also define the optimal graphic types (e.g., stoplight, gauge, arrows, etc.) for the metric. Careful attention to development of these elements will be key to the organization’s effectiveness.
  4. Develop a Centralized Data Warehouse
    The data warehouse will be used to store data, models, metrics and KPIs that will provide the central, consistent source of information for the enterprise.
  5. Employ Data Mining Techniques 
    Data mining helps to unearth hidden relationships in the data. While in the past these techniques were the sole property of academic and research organizations, they are now available to the public through Microsoft Excel windows into properly-designed data warehouses. These techniques enable users to analyze key influencers in the case of various metrics (e.g., the major drivers behind PMPM costs). They perform cluster analyses that can, for example, identify clinical services that are commonly ordered together by a particular physician. They allow sophisticated time series forecasting to identify cycles and trends in utilization. And the results can be provided directly into the user’s spreadsheet.
  6. Design Reports and Data Delivery Methods
    The best analytics are useless if not delivered to the user in a meaningful way. Some data should be delivered as reports that contain detailed data. Some should appear as graphics on a dashboard that allow a quick overview of many different metrics. And some should be accessible in spreadsheets that allow drill-downs and stream-of-consciousness analysis. Tailoring the delivery system to the user’s needs is key to its effectiveness.