Using Automated Data to Promote Quality

Sheila L. Molony MS, RN
Director, Quality Assurance
Connecticut Community Care, Inc.

Clinical Process Support
•	Data collection instrument is synergistic with clinical processes
•	Data is trustworthy (reliable and valid)
•	Data is meaningful for professional practice
•	Data collection instrument is organized, systematic and comprehensive

Quality in the Electronic Record
•	Client-centered
•	client desired actions are assessed and integrated into the plan of care
•	ability to expand depth of assessment in specific areas based upon client’s unique situation and needs
•	Effective
•	“business rules” will not permit missing or invalid data in key areas

Quality in the Electronic Record
•	Efficient
•	branching of questions facilitates efficient data collection
•	data from various respondents can be combined and compared
•	data collection from remote locations using same baseline information
•	ability to navigate to specific areas of assessment

Data Integrity
•	Cross checks
•	missing identification numbers
•	dates are valid and sequential
•	logically inconsistent responses are not allowed
•	Source and timing of documentation is clear and auditable

Clinically Meaningful Data
•	trajectory of symptoms over time
•	medication usage
•	knowledge of disease and treatment
•	number of health care providers
•	number of visits to healthcare providers
•	prevention and self-care practices
•	perceived health status and client perception of primary diagnosis

Population Level Data
•	% clients in hospital
•	% clients in skilled nursing facility (SNF)
•	length of stay in hospital or SNF
•	% of clients with advance directives
•	% of clients seeing a primary care provider in past year 
•	% of diabetic clients receiving ACEI’s or retinal exams

Clinically Meaningful Data
•	Characteristics of client population
•	age
•	ADL/IADL dependencies
•	medical and psychiatric diagnoses and co-morbidities
•	mental status and cognition
•	behavioral symptoms
•	level of social support
•	symptoms of depression

Analysis Support
•	Identify clinical risk factors and needs
•	> 10 medications
•	medication interactions
•	“high risk” medications
•	fall risk factors
•	nutritional risk factors
•	risk for skin breakdown
•	pain assessment
•	life stressors

Analysis Support
•	Relate clinical data to outcomes
•	look at relationships between social support, medical diagnosis, level of function, cognition, self-care practices and service needs
•	examine predictors of hospitalization
•	incorporate client preferences and desired actions
•	“domain” stability and interventions related to outcomes

Targeted Analysis of High-Risk and High Volume Issues
•	High risk or “special needs” clients
•	appropriate identification
•	appropriate referral
•	appropriate consultation and collaboration
•	Utilization of high volume or high-cost equipment or service
•	Timeliness of service initiation

System Improvements
•	Track activity-based time
•	Track client-specific activity
•	Track intensity of service and coordination needs
•	Work flow, scheduling and delegation

Facilitate Best Practices 
•	Reminders and alerts
•	Program eligibility and compliance reminders
•	Data sharing with providers
•	Prevention reminders
•	Chronic disease reminders
•	Support for clinical decision making
•	Practice guidelines

Continuous Quality Improvement
•	timeliness of assessments
•	timeliness of start of services
•	timeliness of client contacts
•	customer satisfaction by service and provider
•	service authorization vs. service utilization
•	service utilization as a function of client capacity and risk-adjusted clinical indicators
•	claims rejections sorted by provider, care manager, reason

Audit for Compliance with Documentation Standards (Remote Record Review)
•	Problems identified during assessment must be addressed in the analysis and plan
•	Formal and informal services are linked with an identified need
•	Goals are client-centered, action-oriented, realistic and timed

Facilitate Behavior Change
•	unmet needs or unmet goals 
•	frequency and scope of interventions
•	under-utilization of data components (domains, leaves, questions)
•	efficiency and effectiveness of care delivery system
•	how does assessment data relate to analysis, identification of needs, goals, interventions and outcomes?