EMHS utilizes a number of data analytics approaches to leverage their information in support of decision-making. Interviews with various staff and stakeholders illustrated how data analytics tools and query/report processes were used to pinpoint patients that required additional attention, identify risk, or measure improvement. This section highlights two areas where data analytics tools are used: (1) EMHC uses the tool Home Health Gold for quality assurance and performance improvement, and (2) HIN is leveraging its data to support population health in support of the ACO.
Home Health Gold
EMHC division utilizes a tool called Home Health Gold, which is a data scrubber, and analytics tool that is interfaced with the home health EHR. The Home Care Quality Assurance/Performance Assurance director uses the information to regularly assess clinical, operational and financial data.
Figure J-6 shows the Home Health Gold Dashboard. The tool can analyze, report, and trend various types of data including:
- OASIS outcomes (before being updated on the CMS Home Health Compare web site);
- Clinical and quality outcomes based on OASIS data;
- Utilization of therapy services;
- Case mix level;
- Hospitalization and ED rates;
- Risk factors;
- Inconsistency in documentation between the clinical record and OASIS; and
- Related financial data.
The Home Health Gold data can be viewed by home care/hospice site or in aggregate for the division. The analysis is reviewed and discussed weekly with the EMHC care management team. Monthly and quarterly reports are reviewed, changes monitored and data reported on a score card.
FIGURE J-6. Home Health Gold Dashboard
HealthInfoNet Population Analytics
HIN is moving toward population health management including support for the Northern New England ACO Collaborative (multi-state and multi-provider) and their need for analytics at a broader community/population level. HIN is focusing on data analytics to drive changes in care. For examples, HIN is analyzing data in such areas as:
Services utilization -- e.g., ADT events allow HIN to track hospital/ED utilization and rehospitalization rates).
Patterns of care -- advanced analytic techniques are used to find patterns and predict behavior.
Comparisons -- compare doctors, their outcomes and ordering patterns to determine.
Risk monitoring -- track patient risk scores and the impact of new clinical data on the score which could support emerging programs in patient risk score profiles.
The potential for leveraging the data of standardized assessments used by LTPAC providers to support population analytics was discussed during the site visit. For example, the OASIS and MDS have provided LTPAC organizations a wealth of data for analytics programs. LTPAC organizations large and small have been harvesting the data to assess quality, performance and risk. As HIN continues to move into big data analytics for population health inclusion of the OASIS and MDS data and the patient assessment summary could provide a rich source of information.