Population Explorer

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Product Overview

The Population Explorer application is used by healthcare personnel responsible for tracking, reporting, and analyzing population metrics to improve care. This group includes hospital administrators, clinical and operations directors, health plan utilization management and quality management directors and members of quality improvement teams; it may also include data architects, data analysts, and knowledge managers. The application facilitates surveillance and reporting of key outcomes such as LOS, cost, and readmissions for selected populations; helps deliver insight into patient cohorts and improvement opportunities for clinical improvement projects; and supports leaders’ ability to identify, prioritize, and report on quality improvement efforts.

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Features

  • Hundreds of predefined patient registries—and the ability to define and save new ones.
  • Starter set of 50 common metrics, each of which can be stratified by diverse types of patient/member data: demographics, payer information, provider, diagnoses, location of treatment, discharge status, and so on.
  • At-a-glance comparisons of readmissions, LOS, or costs between two subsections of a population or between two different populations.

Benefits

Benefits Include:

  • Increased access to data (via self-service) and greater ability to analyze readmission, LOS, and cost metrics for many specific populations.
  • Greater insight into variation as it relates to patient/member demographics, treatment locations, payers, providers or referral sources.
  • More effective quality improvement projects: the organization can focus on the right population and refine the questions they want to answer for that population.
  • Faster access to meaningful information and less frustration: simple, direct, dynamic, interaction with analytics applications replaces cumbersome, slow, report request process.

Measures

Demographics Measures Include:

  • Discharges by age
  • Discharges by gender
  • Discharges by comorbidity count
  • Number and percentage of discharges per ethnic group

Visit Outcomes:

  • Number discharges by patient type
  • Number of discharges by diagnosis

Two-Population Comparisons by:

  • 7-, 14-, 30-, 60-, 90-, and 120-day readmissions
  • Length of stay
  • Charges, payments, variable cost, and total cost

Associative Measures:

  • Patient count, percentage of patients, discharge count, or percentage of discounts per: Patient type, discharge unit, admit source, and discharge status; Financial class and primary payor; Admit ICD code and Primary ICD code

Primary-care visits by:

  • Patient
  • Clinic, and how many per clinic
  • Days since last encounter

See Sample Screenshots of Population Explorer

Data Sources

  • The application uses claims and billing data from patient episodes or visits; it may include EMR reference data (master file facility, departments, payer, cost centers, providers, and APRDRGs), inpatient hospital billing data, discharge status, and so on.
  • Alternate Data Sources: Costing, Billing and Clinical EMR, standard; Cliams data only.

Product Explorer: A Deeper View

Background

As healthcare teams identify opportunities to reduce variability in cost and quality in specific clinical and operational areas, they also need to understand the affected patient populations before they can move forward in designing effective quality improvement projects. Typically, the complex questions of improvement teams must be transmitted to a data analyst, who then conducts the queries and produce a custom-built report—one that will likely require several iterative reviews before it meets the needs of the team. This process of iteratively defining patient/member cohorts can be time-consuming—and it delays progress on meaningful clinical improvement and research.

What types of problems does Population Explorer address?

In most healthcare systems and health plans, analyzing population metrics to improve care is difficult and time-consuming, requiring analyst staff to collect and combine data from numerous sources to satisfy each request. Organizations can benefit from a tool that pulls together patient/member data from multiple sources to help teams understand population characteristics such as demographics, payer information, provider relationships, counts of current diagnoses, and other visit-related outcomes.

Users

Population Explorer is intended for those responsible for tracking, reporting, and analyzing population metrics to improve care. This usually includes hospital administrators, clinical and operations directors, health plan utilization management and quality management directors and members of quality improvement teams. It may also include data architects, data analysts, and knowledge managers. When deciding who should be trained to use Population Explorer, consider choosing people who fit these general role profiles:

  • Staff who are responsible for tracking and reporting LOS and readmission, or other similar.
  • Staff who will help define the cohorts for clinical improvement projects.
  • Clinical and administrative leaders responsible for identifying, prioritizing, and reporting on quality improvement efforts.
  • Any staff or leaders who may want to delve deeper than static reports to identify trends and potential improvement areas.

Use Cases

  • A Cardiovascular quality improvement team wants to identify the next opportunity they should focus effort on. Using Population Explorer, they determine that their 30-day readmission rate for heart failure patients is, at 28%, significantly higher than the national average. They determine their next improvement effort must focus on reducing readmission in their heart failure population registry.
  • The health plan’s quality management director is preparing for the next CMS data submission. The All Cause Readmission Rate was the target of multiple recent care management programs and she would like to compare results across providers, time frames and diagnosis.
  • The quality improvement team wants to look more closely at their COPD patients, comparing costs and readmission rates for individuals age 65 and older to those younger than 65.
  • The Cardiovascular team wants to compare the 90-day readmission rate for Medicaid patients with heart failure to all other patients with heart failure; they also want to review the average charges and LOS for the two patient populations.

Anticipated Improvements

  • Increased access to data (via self-service) and greater ability to analyze readmission, LOS, and cost metrics for many specific populations.
  • Greater insight into variation as it relates to patient demographics, treatment locations, payers, or referral sources.
  • More effective quality improvement projects: the organization can focus on the right population and refine the questions they want to answer for that population.
  • Faster access to meaningful information and less frustration: simple, direct, dynamic, interaction with analytics applications replaces cumbersome, slow, report request process.

Success Measure Examples

  • Increase Efficiency: Decrease the number of report requests for simple metrics on various different population registries.
  • Increase Access: Share data and make it available for data-driven decisions.
  • Identify Opportunity: Identify length of stay for a specific patients within any of hundreds of registries.
  • Compare Performance: Compare performance on readmission, LOS, and financial metrics between different registries, admit sources, visit details, financial class, payers, providers, discharge location, or billing location.