Functional Job Analysis and Health Care Management Mark Mackay - - PDF document

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Functional Job Analysis and Health Care Management

Mark Mackay

Department of Health Care Management June 2013

Functional Job Analysis (FJA)

• An approach of measuring work
• Developed by US Employment and

Training Administration

• The notion was to produce standardised
• ccupational information relating to the

– The worker, and – The job (role).

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FJA (cont.)

• It’s a Quantitative approach
• Relies upon a taxonomy or inventory of the various

functions that compose the work

• Assumption was that any job had 3 broad

components:

– Data – People – Things.

• Components expanded to include:

– Worker Instructions – Reasoning – Maths, and – Language.

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FJA – in Health

• Frank Moore applied FJA to tasks in health

(family planning)

e.g., Moore, F. I. (1972). Dictionary of family planning job tasks. New Orleans: Tulane School of Public Health.

• Steve Fine applied it to human services

workers

e.g., Fine, S. A. (1988). Human service workers (Chapter 10.2, pp. 1163-1180). In S. Gael (ed.), The job analysis handbook for business, industry, and government, Vol. 2. New York: Wiley.

• Cronshaw and Best applied FJA to job

redesign in Veteran Affairs (hospital and primary health) (US)

e.g., Cronshaw, S. F., Best, R., Zugec, L., Warner, M. A., Hysong, S. J., & Pugh, J. A. (2007). A five component validation model for functional job analysis as used in job redesign. Ergometrika, 4, 12-31. 4

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FJA in Australian Health

• We wanted to know what medical practitioners did in

hospitals (Castle & Mackay)

• We knew that this work was usually characterised by

worker mobility and task fragmentation (see Mejia, Morán, and Favela, 2007).

• Were given access to the work undertaken by

Cronshaw et al in Veterans Health

• Adapted their taxonomy of job description from a US

primary health care setting to suit an Australian acute care setting

• Applied it

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FJA + Temporal Analysis

• An important difference between the

past FJA work and the work undertaken in Australia was in the introduction of temporal analysis

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The Observation Process

• Training Job Analysts/Observers
• Information dissemination to potential participants
• Pre-observation questionnaire – role expectations, barriers to safe patient

care, levels of satisfaction with ways of work organisation

• Observing doctors using electronic purpose-built tracking tool

– Observations by the minute according to task taxonomy (based on purpose of activities) – Work impact options – interruptions, activity blocks, OACIS

• Post observation questionnaire – experience of tracking, validity, unfinished

work

• Work context – purpose of unit, structure, staffing, research, achievements,

vacancies

• Analysis – individual reports and presentation of unit results (focus group–

impediments and solutions)

• Final report for the Unit Head

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The Tracking Tool

The tracking tool was comprised of two components:

• Rigid task taxonomy

– 6 Functions – 30+ Sub-functions – 130+ outputs (or activities)

• Flexible Work Impact Options

– tailored to suit departments and purposes

Around people (e.g., contacts, communications, interruptions) Around data (e.g., access to OACIS) Around things (e.g., sourcing, waiting)

Electronic collection

– Collect more data – Collect more frequently (1 minute observations) – Clean collection *

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Task Taxonomy

FUNCTIONS SUBFUNCTIONS CLINICAL SERVICE Pre-patient Intervention Patient Assessment and Monitoring Patient Treatment Patient Coordination Preventative Healthcare Patient Education Medication Management Co-ordination of Patient Care Assist with activities of daily living ADMINISTRATIVE DUTIES Patient Records and document control Exchanging Information/Meetings Administrative Support Unit management Corporate Marketing Legal matters 300 LOGISTIC SUPPORT Clinical Set-up /Maintenance Supply Maintenance Maintaining Equipment Downtime due to travel or waiting Hospitality services 400 WORKFORCE DEVELOPMENT Training/Supervising Mandatory training Continuing Medical Education Performance Management & counselling Recruitment OHS&W 500 RESEARCH Planning Research activities Reporting and publishing 600 BREAKS Breaks

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Work impact options

• Where is the activity occurring?
• What interruptions occurred in this one minute interval?
• What type of interruption?
• When was the pager/person responded to?
• Was OACIS accessed to perform the task?
• Did the patient change?
• Was the phone call necessary?
• Did the handover process follow the recognised format?
• How was the transfer of information about the patient between doctors and nurses

performed?

Options selected, adapted to fit the PURPOSE

• f study

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Results

• Number of observations

– 5-10k observations per trial – Total observations 20k+

• Clean data – almost no missing records
• At least 95% of planned tracking completed for

each role

• Observations/ Tracking –reflected work generally

representative of role (including roles that work around the clock)

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Did we do enough tracking?

Stacked Mixture Graph: Unit Functions

0% 20% 40% 60% 80% 100% 120% block 1 block 3 block 5 block 7 block 9 block 11 block 13 block 15 block 17 block 19 block 21 block 23 block 25 block 27 block 29 block 31 block 33 block 35 block 37 block 39 block 41 block 43 block 45 block 47 block 49 block 51 block 53 block 55 block 57 block 59 block 61 block 63 block 65 block 67 block 69 block 71 block 73 block 75 block 77 block 79 block 81 block 83 Time Block (2 hours) % of observations 100 Service Delivery 200 Administrative Duties 300 Logistic Support 400 Workforce Management 500 Research 600 Rostered & other breaks stability

Stability of the function mix achieved at 125 hours for the unit

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Proportion of time by function

Variation of function profile noted across the surgical and medical units. Minimum of 50% of time spent on service delivery.

Proportion of Time Spent by Function

100 Service Delivery 62% 200 Administrative Duties 9% 300 Logistic Support 8% 400 Workforce Management 16% 500 Research 0% 600 Rostered & other breaks 5%

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Sub-functions

Sub-functions as a percentage of total observations

0% 5% 10% 15% 20% 25% 120 Patient Treatment 110 Patient Assessment 130 Patient Coordination 410 Training/Supervising 340 Downtime due to travel or waiting 610 Breaks 440 Performance Management 100 Pre-patient Intervention 220 Exchanging Information/Meetings 150 Patient Education Other (12 sub- functions) Sub-functions % of observations

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Activities / outputs as a percentage of

• bservations

Sum total of all activities performed infrequently

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Where activity was observed

theatre theatre theatre theatre theatre

Outpatients clinic Emergency Department On the ward

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Work-flow patterns

Work Flow Pattern: Where, Functions and Interruptions

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 7:50 8:08 8:26 8:44 9:02 9:20 9:38 9:56 10:14 10:32 10:50 11:08 11:26 11:44 12:02 12:20 12:38 12:56 13:14 13:32 13:50 14:08 14:26 14:44 15:02 15:20 15:38 15:56 16:14 16:32 Time Where/Function/Interruption

ED Office On the ward Other Seminar Theatre Breaks Research Workforce Logistics Admin Clinical respond to interruption interruption

Shows where work occurred, what function was

• ccurring and what interruptions occurred

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Cumulative work-flow patterns - intensity

Work Intensity

1 4 7 10 13 16 19 22 25 28 31 34 37 40 43 46 49 52 55 58 61 64 67 70 73 76 79 82 85 88 91 94 97 100 Record No. (minute intervals) Work Intensity Activity changes Patient changes Location change Interruptions Responses Cumulative effect of task changing and/or patient changing and/or interruption occurring and/or responding to an interruption – concurrent over 100 minutes of one person’s work

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Changing task focus

Comparative Work Intensity - duration before change: Sun, Wed and Sat

0% 10% 20% 30% 40% 50% 60% 70% 80% 90% 100% 1 2 3 4 5 6 7 8 9 10 more than 10 Duration before change (min.) Cum % Wed Sat Sun 90% or more changes occur in 6 minutes or less

On average 50% of change occurs within one minute

Some Management Implications

• Confirmed staff are mobile and tasks are

fragmented

• Data helped quantify extent of anecdotal

concerns (e.g., interruptions and research)

• Showed managers didn’t always understand

what staff were doing (in significant ways)

• Enabled realignment of duties (e.g., RMO got

extra theatre time in order to progress to registrar training program)

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Implications for Health Care Management

• In general the concept of understanding how

staff are deployed is considered important in most industries

• In many health care settings work is complex

and understanding exactly what workers do, and when and where they do it, is not always clear

• The management of human resources is

becomes more uncertain in such circumstances (indeed part of a simulation project)

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Implications for Health Care Management Teaching

• Application of Lean Thinking, Job Redesign,

Business Improvement, etc. has been used to improve the understanding of work and flow in health settings.

• FJA – worker centred, so offers a different

perspective

• Turns anecdotes and myth into data (evidence)
• Can help demonstrate:

– A greater understanding of the role of particular professions &/or jobs – The challenges of management in a health care setting

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What about the Future?

• Worker tracking was expensive
• New technology provides different options for

measuring work

– Using mobile phones

• For example – epicollect – open source free tool for

collecting geo-tagged data from mobile phones (www.epicollect.net)

• Chen and Stankovic have developed smart phone + body

sensor recording to enable the logging of person-to-person interaction (www.cs.virginia.edu/~stankovic/psfiles/qiang-social.pdf)

– Sensors have been developed for monitoring hand washing (news.msn.com/science-technology/hospitals-using-tech-to-track-workers-hand-hygiene) – Sociometric sensors also developed(www.sociometricsolutions.com)

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The Future (cont.)

• Video observation is not new

E.g.. Williams KN, Herman R, Gajewski B, and Wilson K (2009). Elderspeak Communication: Impact on Dementia Care. American Journal of Alzheimer’s Disease & Other Dementias, Vol 24(1) 11-20.

• New methods of analysis are being developed (e.g.,

Rob Roy - MIT)

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• The new technologies allow for much richer type of data

collection

• The potential is great, but are we ready for it is another

question

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Questions For further information contact: mark.mackay@flinders.edu.au