Measuring Capacity to Care Using Nursing Data

Front Matter
Copyright
About the authors
Preface
	Organization of the book
Acknowledgments
Dynamic health care environments
	How is capacity to care defined?
	Healthcare environments
	What influences the capacity to care?
		Leadership and governance
		Healthcare financing
		Health workforce
		Medical products, devices and technologies
		Health service delivery
		Information and research
	What are the desired health system outcomes?
		Improved health, efficiency, responsiveness and caring
	Nursing data at the center
Health care operational inefficiencies: Costly events
	Workforce management
	Nursing workloads and nurse staffing methods
	Measuring operational activity and efficiency
		Care recipient characteristics
		Types of resource input
		Healthcare activity processes
		Measuring health outcomes
	Learning health systems
		Making better use of data and information
	Operational research
Digital transformation needs to measure nursing and midwifery care demands and workloads
	What determines nursing workloads?
	Nurse staffing methods in use or recommended
	Methods in use to measure nursing care demand
		Nursing Hours Per Patient Day
		Nurse staffing ratios
		Patient/client types
		Patient classification
	How do nursing service demand measurement methods compare?
		Patient type and treatment protocol patterns by clinical speciality
	Variables influencing nursing service demands, workloads, and costs
	Information flows and patient/client journeys
	Digital transformation enabling nursing data inclusion
		Nursing minimum data sets
		Nursing data and standards
		Reference terminologies
	Use of metadata
	Nursing service demand metadata
		Service capacity - Identifying required nursing skill mix
		Service capacity - Nursing working conditions
		Admission and continuing service determinants
	Indicators of nursing care demand
		Metadata enabling the evaluation of nursing service contributions relative to patient outcomes
	Nursing workload management metadata need
	Optimizing workplace efficiencies
		Political, professional, managerial, and industrial influencers
	Conclusion
Nursing and midwifery work measurement methods and use
	Describing nursing work
		Boundaries or scope of nursing/midwifery practice
	Analyzing nursing work to be measured
	Work measurement methods
		Nursing staff availability and performance - Input variables
		A nursing practice taxonomy - Process variables
		Time study methodology
		Self-recording of nursing activity
		Work sampling methodology
		Professional judgments/estimates
	Conversion of work measurement data to a workload measure
	Making use of study results
		Using workload measurement systems with established time standards
		Nursing workload measures' validity
	Nursing work measures in use
	Patient classification principles
	Developing national nursing service weight measures
	Evidence of acuity link with patient outcomes
	Future directions
Identifying skill mix needs
	Matching available skills with service demands
	Addressing qualified nurse staffing shortages
		Working with a varied skill mix
		Working to scope
	Current skill mix identification methods
		Specializations and competencies
		Occupational classifications
	Nursing industry awards, agreements and skill mix
	Job evaluation and skill assessment methods
		Skills Framework for the Information Age (SFIA)
	Education and professional development contributions
	Nursing career pathways
	Re-engineering clinical services using non-nursing support staff
		Example
	Future directions for identifying and matching skill mix needs with available staffing resources
Nursing and organizational models of care
	Factors known to influence nursing models of care
	The nursing process - Conceptual base for nursing practice
		Nursing care plans
		Functional or task allocation
		Patient allocation
		Primary nursing
		Team nursing - A collaborative model of care
	Small team nursing
		The benefits of small team nursing
		Leading the change
		The shift routine example
		Evaluate success of team nursing implementation
	Inter and multidisciplinary models of care
	Organizational models of care influencing patient outcomes
	Success factors
Staffing resource allocation, budgets and management
	Using demand side organizational nursing and midwifery workforce planning methods
	Professional and government nurse staffing initiatives
	Rostering fundamentals
	Data variables required to calculate nurse staffing needs
	Projecting nursing service demand and workforce requirements
	Calculating departmental/unit nurse staffing requirements
		Use of nurse:patient ratios to capture FTE/WTE measures for clinical care
		Use of Nursing (Care) Hours Per Patient Day (NHPPD)
		Use of patient acuity data
			Using patient demand measures to calculate staff establishments
	Staffing needs for other service types
		Day only departments
		Obstetric services
		Geriatric, disability and rehabilitation residential services
		Operating theaters
		Accident and emergency departments
		Specialist outpatient departments
		Supervisory and administrative clinical staff
	Significant variations resulting from method used
	An international patient type HPPD benchmarking research study
	Rostering methods
		Foundations for roster development
		Cyclic rostering
		Self rostering
		Request focus rostering
		Rostering process
		Rostering principles
		Evaluating the suitability of rosters
		Roster reengineering
	Workforce availability
	Financial management
		Roster budgeting processes based on service demand
		Staffing establishment budgeting processes
		Zero based budgeting
		Activity based costing (ABC)/funding (ABF)
	Casemix definitions (hospital `products)
		Use of casemix classifications and nursing service costs
	Connectivity requirements for nursing resource management
		Linking electronic health records with nursing resource management
	Capturing and using the data operationally
Workforce planning
	Nursing and Midwifery Workforce Statistics
	Nursing and midwifery's future perspectives
	Nursing workforce structures and statistics
	Nursing and midwifery workforce education and professional development
	Workforce planning models and tools
	Recruitment to the profession
	Workforce participation
		Employment characteristics
		Retention and turnover rates
		Causes of dissatisfaction and turnover
	Replacement and succession planning
	Meeting future demands
Digital health ecosystems: Use of informatics, connectivity and system interoperability
	A need to resolve data issues
	What is a digital health ecosystem?
	Essential ecosystem features
	Healthcare ecosystem connectivity frameworks
	Today's state of the art
		Shadow systems and health data
		Connectivity and interoperability
		Measuring interoperability
		Interoperability standards and schema
		Computing platforms
	Interoperability, clinical needs and secondary data use
		Using source data and information for multiple purposes
		Decision support systems - Using secondary data
		National and international health data uses
			National and international reporting - An example
		Genomics data and personalized medicine
	Gap analysis and digital transformation
	Conclusion
A digital transformation strategy enabling nursing data use
	System implementation and change management
	Changing organizational digital health infrastructures
		Common barriers
	Using `Lean and `Six Sigma techniques to design new work processes
	Potential use of nursing data
		Patient acuity/nurse dependency/nurse-patient ratios
		Work hours per patient day/visit/procedure/attendance/birth/occasion of service/operating minute etc.
		Workload management
		Workforce planning
		Care capacity management
		Pathways and care plans with outcome reporting
		Nursing intensity measures
		Retrospective and proactive discharge analysis
		Diet ordering
		Rostering for clinical and non-clinical departments
		Clinical handovers
		Allied health intervention register and reporting
		Patient risk assessments with action plans
		Human resource management registers and staff health profiles with reports
		Staff health system
		Efficiency measures/benchmarking all departments
		Patient acuity and workload management system implementation project plan - A generic example using legacy systems
			Aim of the plan
			Objectives of the plan are to provide
			Organizational benefits of implementing the system
			Scope of project
			Project priorities
			Project prerequisite
			Software development project team
				Project lead: Primary (lead) and secondary
				IT support: Primary (lead) and secondary
				Clinical support
			Governance structure
				Hospital project sponsor
				Project manager
				System co-ordinator/administrator
				IT lead for the project (organization wide)
				Technical lead (hospital based)
		Executive lead for motivational strategy
		Resource allocation and task allocation for system implementation
		Risk assessment
			Risk rating matrix scale
	Desired outcome measures benefitting nurses and their patients
	Data collection methods
		Measuring patient acuity on a shift
	Local nursing acuity data use
		Allocating staff to workloads
		Handovers
		Workforce planning
	Ward/unit manager/senior nurse daily routines to ensure data accuracy
	Health IT evaluation methods
	A nursing workload management system and change management evaluation framework
Measuring health service quality
	What is quality?
		Quality programs
	Nursing practice environments influencing quality
		Collegial cultures
	Data quality
		Health data uses and links to nursing data
		Using data to support decision making
		Data sets and data repositories
		Data governance mechanisms
	Standards, accreditation and governance
		Accreditation standards
		Types of standards
		Standards governance
	Reliability and quality measures associated with patient acuity data
	Clinical data management issues
	Outcomes research and big data
		Performance indicators and health system frameworks
		Measuring caring as an outcome measure
		Impact of funding arrangements on the selection of performance indicators
	Big data management and governance
		Health quality measurement issues
		References
Residential and community care management
	Introduction
	Residential care environments
	Measuring care service demand and funding mechanisms
	Residential service work measurement methods and outcomes
	Identifying skill mix needs
	Organizational and nursing models of care
	Staffing resource management
	Aged care workforce planning
	Use of informatics, digital transformation
	Documentation, reporting and change management
	Measuring service quality
	Qualify of life-future vision
	References
Current and future vision
	Global health and capacity to care
	Nurses and midwives' unique contributions to global health
	Our digital health ecosystem
	Measuring health system effectiveness
	Hospital performance statistics and costs
	Safe patient care vs costs
	Benefits from using nursing data
	Optimizing our capacity to care in a sustainable health system
		Close the loop between resource flows into and out of the system
		Nursing workload analysis
		Nursing and midwifery work characteristics and measurements
		The nursing and midwifery workforce
		Digital transformation needs
	A future vision
	References
Case study 1 — Patient Assessment and Information System (PAIS): Work measurement research and workload measurement method ...
	Study purpose
	Original sample
		Methods
		Research objectives
		Original study design
		Data analysis
		Findings
		Results
		Staffing methodology development
		PAIS implementation and use
	Use of PAIS in New South Wales, Queensland and Western Australia
	Discussion
	Issues encountered
	Political interference
	References
Case Study 2 - Design, development and use of the TrendCare system
	Study purpose
	Original sample
		Research objective
		Methods
		Study design
		Data analysis
		Findings
		Results
		Staffing methodology development
		TrendCare implementation and use
	Discussion
		Validation and endorsement
	Lessons learned
	References
Index
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