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Demand, Capacity, Activity and Queue
(DCAQ):
A Case Study in Borders Alcohol & Drug
Partnership (ADP)
Biba Brand, National ADP Delivery Advisor, Scottish Government
Fiona Doig, ADP Strategic Co-ordinator, Borders ADP
2
Acknowledgements
With thanks to:
• Scottish Government Quality & Efficiency Support Team (QuEST) for their
support;
• Service Managers and staff who participated in the use of QuEST’s Demand
Capacity Activity Queue (DCAQ) Tool in their organisations;
• Susan Walker (Borders ADP) and Rachel Tatler (Scottish Government) for
their help in preparing this document.
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Table of Contents
1 Introduction 4
2 Background 4
2.1 Overview of the Investment Review 4 3 Delivering the Investment Review – Examination of the local system 5
3.1 The ‘project team’ 5
3.2 Data and evidence collection 6
4 Review of service uptake data 6
5 Using the DCAQ Tool 7
5.1 Areas for agreement 8
5.2 Timing 8
5.3 Data collection 8
5.4 Other areas of clarification 9
5.5 Data Management 9
6 Case Study 10
6.1 Data Gathering 10
6.2 Demand 10
6.3 Capacity 10
Figure 1 – DCAQ Data 13
6.4 Analysis 15
Figure 2 – DCAQ – Summary Data Analysis 18
7 DCAQ Projecting Hypothetical Impact 20
Figure 3 – Demand vs. Capacity (Actual vs. Scenario) 21
Figure 4 - Time Lost by the Service Due to DNAs (Actual vs. Scenario) 22
Figure 5 - Overall Balance between Capacity and Demand (Actual vs. Scenario) 22
8 Setting Improvement Goals 23
9 Learning Points 24
10 Impact of QuEST 25
11 Conclusion 26
References 27
Appendix 1 – Mental Health DCAQ Tool 28
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Demand, Capacity, Activity and Queue (DCAQ) – Case Study in Borders
Alcohol & Drugs Partnership (ADP) Services
1 Introduction
This paper presents a case study in the use of the Scottish Governments Quality and
Efficiency Support Team (QuEST) Demand, Capacity, Activity and Queue (DCAQ)
tool in Borders ADP. The DCAQ Tool was used to collect data during the Borders
ADP Investment Review. The paper outlines a short overview to the Investment
Review and outlines the overall data collection process for capacity and demand
analysis which took place during the second phase of the review. Section 4 onwards
concentrates on the use of the DCAQ Tool and using data from one service to
illustrate opportunities for improvement. Section 5 reflects on future role of the Tool
in Borders.
2 Background
In July 2012 the Borders ADP commenced work on an Investment Review. Borders
ADP cover a co-terminus local authority and NHS Board. Its population is 114,000.
The ring-fenced ADP budget for 2013-14 is £1,342,790.
The Investment Review was a response to the shifts in emphasis towards prevention
and early intervention and a recovery focussed system of care. Alongside these
shifts in emphasis are the constraints on public spending and awareness that public
sector services are expected to improve quality while at the same time ensuring
efficiencies.
The aim of the Investment Review was to review and make recommendations about
future investment of funding for Interventions and Services, as part of this, the use of
information on service demand and capacity was felt to be useful to the Review
process.
2.1 Overview of the Investment Review
There were three stages to the review:
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i) External consultancy – this highlighted gaps in local knowledge relating to
need and service uptake
ii) Examination of the local system - in terms of need and services/interventions
in place to respond. It was as part of this requirement that the QuEST Tool was
used. These findings were reported in the Investment Review Report, May 2013.
ADP Investment Review Report - Final, May 2013.pdf
iii) Consultation on Review Recommendations and development of Border’s ADP
Future Model - based on this second round of consultation the ADP Executive Group
developed a Future Model which was approved by the ADP in August 2013.
ADP Future Model of Investment- August 2013.pdf
3 Delivering the Investment Review – Examination of the local system
3.1 The ‘project team’
The Executive Group identified that additional skills and experience would be crucial
in ensuring a robust examination of the local system. Scottish Borders Council
Social Work Department therefore identified capacity from a Business Analyst to
provide support relating to data collection and analysis.
The Scottish Government’s National ADP Delivery Advisor agreed to offer support to
this phase of the Review.
The other members of the team were the ADP Strategic Co-ordinator and ADP
Development Officer with an ‘as required’ attendance from the Social Work Group
Manager for Mental Health and Addictions.
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In addition, Scottish Drugs Forum Quality Team provided support to deliver Service
User focus groups.
3.2 Data and evidence collection
A plan of data and evidence sources and range of approaches was identified to
inform a ‘whole system approach’ to reducing harm from alcohol and drugs including:
i) Review of existing and available data sets relating to prevalence and need
ii) Review of service data: analysis of demand and capacity data, number of
clients, planned and unplanned discharges. During development of this plan the
National ADP Delivery Advisor discussed with the Project Team that the Mental
Health QuEST DCAQ Tool could be a potential method for analysing demand and
capacity. At that time the Tool had only been used within mental health services.
Permission was sought from QuEST to allow use of the DCAQ Tool. It was agreed
to approach Service Managers via one of the scheduled meetings to seek
agreement to use the Tool in their services.
iii) Stakeholder evidence and involvement: including themed Focus Groups with
colleagues, stakeholder interviews, substance misuse services managers meetings
and service user Focus Groups.
iv) Review of key policies and guidance notes.
Findings from all these sources were then used to develop recommendations for
each of the themed areas. These recommendations aimed to inform the way
forward.
4 Review of service uptake data
Data was gathered via Services monitoring reports and the Drug and Alcohol Waiting
Times Database. This included:
• Number of referrals
• Numbers offered assessment but never attend
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• Did not attend (DNA) rates for assessment appointments
• Numbers commencing treatment (following completion of assessment)
• Retention over 12 weeks
• Rates of planned and unplanned discharge
The QuEST team amended the DCAQ Tool to change the wording from ‘patient’ to
‘client’ to reflect its use in health and social care settings.
5 Using the DCAQ Tool
In Appendix 1 the data sheet for the Mental Health DCAQ Tool developed by the
Scottish Government’s Quality and Efficiency Support Team (QuEST) is displayed
for measurement of capacity, demand, activity and any queue. This was amended
for the purposes of work with Borders ADP, where not only NHS but local authority
and third sector social care support would be involved.
At a Service Managers’ meeting all services were asked if they would be willing to
complete the questionnaire. There was agreement in principle for this and a further
two hour meeting was arranged.
At this meeting in February 2013, the National ADP Delivery Advisor met with ADP
staff and Borders ADP services (five organisations, eight services). There had been
several previous meetings with ADP staff in the lead up period.
The purpose of this meeting was to achieve the following points:
1. Explain the thinking behind the tool as an improvement model,
2. Explain and agree consistent definitions,
3. Explain any questions and
4. Gain agreement to proceed,
5. Agree which 12 week period to use for capturing data retrospectively, and
6. Agree a timescale for reporting back.
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5.1 Areas for agreement
There were various areas for agreements raised by service managers at the
meeting. The following section is a summary of the key points:
5.2 Timing
Two of the services had recently experienced staffing vacancies which had severely
impacted on their capacity to deliver. There was concern from those services that
collection of data in the most recent quarter would not reflect ‘normal’ service activity.
Services were also concerned about the agreed timescales for feedback on the
Tools (1.4.13).
5.3 Data collection
DNA’s: Not all services routinely collected data on DNA’s and there was concern
about how they would achieve this. The ADP Development Officer agreed to use the
Drug and Alcohol Treatment Waiting Times (DATWT) Database to elicit DNA rates
for assessment appointment on behalf of the adult services. Following discussion,
agreement was reached on how to gather information on follow-up appointment DNA
rates.
Mileage: Not all services felt confident they could readily access mileage information.
However, on exploring this all agencies identified methods for gathering this data, for
example through staff expenses claim forms.
Allocation meetings: there was not agreement about what should be defined as an
‘allocation meeting’ as some services incorporated some elements of allocation
meetings into team business meetings. Therefore further discussion was required to
keep consistent definitions.
Clarity had to be agreed for services or staff which were, for example, multi-
component and where staff may be deployed in different ways e.g. proportion of time
for clinical versus time for development or management work. It was therefore
agreed that in some circumstances services or staff could be ‘split’ into parts. A
single Tool could be completed for each part of the service. Staff with dual roles,
such as team leaders with half a full caseload should be treated as 0.5 Whole Time
Equivalent (WTE) in respect of the Tool based on their pro-rata direct client contact.
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5.4 Other areas of clarification
There was scepticism from one service in particular about the value of using the
Tool. However, all services agreed to participate in the DCAQ work and did manage
to collect the required data from their existing service information and the DATWTs
Database.
All six expectations were met and all organisations were represented by their
manager or in one case with a manager representative. All services were content to
proceed and agreed the reporting quarter Dec 2012 – Feb 2013 for retrospectively
gathering data. Managers felt they could achieve this within a month’s time period.
Meanwhile, to gain deeper insight in reasons for DNAs, further information was
sought by the ADP Development Officer. This involved undertaking a short study on
barriers to accessing alcohol and drugs services. This involved distributing paper
questionnaires to Service Users (SUs) and also to staff. SUs were also offered the
opportunity to receive a follow-up phone call. Five SUs took this offer up.
5.5 Data Management
Once data was collated it was sent to the ADP co-ordinator, and forwarded to the SG
National ADP Delivery Advisor. Another month was spent cleaning data to keep
consistent and agreed definitions.
The SG Advisor met with the QuEST staff to ensure accurate data analysis and with
ADP staff to go through data and clarify understanding and potential improvements.
SG Advisor and ADP staff then met individually with service managers to confirm
data accuracy and jointly develop service improvement goals.
The following section will focus on one service to illustrate use of the DCAQ tool.
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6 Case Study
6.1 Data Gathering
The data from services was inputted to the DCAQ tool, one for each service. For one
service, Service X, this is outlined in Figure 1 which shows a display of demand,
queue, capacity, activity in the service, and these overlap to show the overall
balance between capacity and demand in Service X.
6.2 Demand
In the agreed 12 week reporting period there were 108 referrals, of which 1 was
referred elsewhere due to an inappropriate referral, 26 individuals waiting for
treatment and care at the end of the reporting period and no service users opted-out
during this period. However, it was noted that a number of these clients had been
seen for an assessment appointment but the assessment had not been completed.
Therefore, they were not strictly on a waiting list.
6.3 Capacity
As part of service capacity, information on DNAs, group and individual work, and the
number and length of appointments is displayed.
DNAs
DNA information was collected and showed that in the reporting period the DNA rate
was 20% for assessment appointments and 30% for follow-up or treatment
appointments. Services agreed prior to data gathering that the first assessment
appointment would constitute the assessment and any subsequent appointments
would be treated as follow-up.
DNA rate for assessment is measured by: the number of first assessment slots
missed divided by number of assessment slots offered, multiplied by one hundred.
DNA rate for follow-up is measured by: the number of follow-up slots missed divided
by number of follow-up slots offered, multiplied by one hundred.
Individual and Group Work Appointments
In terms of the kind of appointment, individual appointments were received by 75%
of SUs, group work sessions were received by 10% of SUs and both group work and
individual appointments were received by 15% of SUs.
In each group work session or series there was on average 5 group work meetings,
attended on average by 7 service users and facilitated by 1 staff member.
Length of Appointment
On average first assessment appointments lasted 1 hour and took 1 hour of
administrative time for updating files. Similarly follow-up or treatment and care
appointments lasted 1 hour but required 20 minutes administrative time. For each
SU there are on average 8 follow-up or treatment and care appointments in any
given 12 week period.
Group work meetings lasted on average 1 hour and required 30 minutes of
administrative time for updating files.
Staffing
There were four WTE staff in this service each working a 37.5 hour week.
Time Allocation for Staff
This section shows how much time each staff member spends, on average, for
example on annual leave (per year), on sickness leave (per year), in supervision
meetings (per month), travelling (per week), at meetings regarding direct SU
engagement or other meetings (per week), and in SU/patient allocation meetings to
assign new clients to a staff member. Allocation meetings would also include the
number of staff attending these.
Allocation meetings were a key area for improvement in mental health services
where these meetings took up significant time. In this latter context capacity was
freed up by the mental health managers taking on this function and allocating new
service users directly to relevant staff members, without the requirement of a
meeting.
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In Service X the full staff team (4.0 WTE) attended two 1.5 hour allocation meetings.
Staff in Service X spend on average 3 hours per week travelling to appointments in
this rural area.
In general, services in Borders had quite a few hours spent on ‘other meetings’ due
to a smaller staff pool and still tasked with strategic and operational cross sectoral
work between substance use and related areas such as homelessness, mental
health, offending, etc. However this service, in the 12 week period, did not have any
hours spent under this heading.
Scenario
A further column has been displayed by the DCAQ tool which is optional, but for
discussion purposes a hypothetical improvement goal was set for DNAs to improve
both assessments and follow-up or treatment and care appointment DNAs, to reduce
to 15% for assessments and 20% for follow-up appointments. A further improvement
goal was set by hypothetically seeking to reduce the number of allocation meetings
from two to one (1.5 hour) meeting per week.
Inserting these scenarios creates a modelling effect to project the impact on waiting
times (queues), staff time freed-up, etc.
More information on this will be provided in Section 5 - DCAQ Projecting
Hypothetical Impact.
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Figure 1 – DCAQ Data 1.1 Time Period for Demand Data
Enter number of weeks you wish to calculate Demand and for (recommended minimum of 12 weeks) 12
1.2 Referral Information - Please enter the data for the time period specified above
ACTUALNumber
Enter number of referrals received in time period 108
Enter number of opt-outs in time period
Enter number of people referred elsewhere as inappropriate for your service in time period 1
Enter number of individuals still on waiting list for assessment at end of snapshot time period 26
Select
Does your service run a separate waiting list for treatment? No
If yes, enter number of individuals on waiting list for treatment at end of snapshot time period
1.3 Did Not Attend (DNA)
Number
DNA rate for 1st assessment slots
calculate as (no. of 1st assessment slots missed/ no. 1st assessment slots offered)*10020.0%
DNA rate for individual follow-up slots
calculate as (no. of follow-up slots missed/ no. of follow up slots planned)*10030.0%
1.4 Group Work
Select
Does your service do group work? Yes
If you selected No, please go to section 1.5
If you selected Yes, please fill in the following:
Number
Percentage of people who only go into individual work 75%
Percentage of people who only go into group work 10%
Percentage of people who receive both group and individual work 15%
Percentages should total 100%, if not, please check your figures ----------- check total: 100%
Average number of sessions per group intervention 5
Average number of people per group session 7
Average number of staff per group session 1
1.5 Slot length, Clinical/Client Admin, and Follow-up
Slot length & Associated Clinical AdminHours
Average clinical/client contact time taken per first assessment 1.00
Average clinical/client admin time taken per first assessment 1.00
Average clinical/client contact time taken per follow-up 1.00
Average clinical/client admin time taken per follow-up 0.20
Average clinical/client contact time taken per staff member per group session 1.00
Average clinical/client admin time taken per staff member per group session 0.30
Follow-Up
Number
Average number of follow-up visits per client 8
Sections 1.3 - 1.5 require you to enter a number of variables in order to calculate the time
needed to respond to the information you have entered above. Use data that best reflects what
usually happens in your service. For guidance and suggestions on how to calculate a
representative value for each variable, contact QuEST via the details provided on the Introduction
worksheet.
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Figure 1 – DCAQ Data (continued)
CAPACITY1.6 Staff Allocation - Per Week
Staff Group <please specify below> Staff WTE
Hours per
WTE Staff WTE
Hours per
WTE
Staff Group 1
Staff Group 2 4 37.5 4 37.5
Staff Group 3
Staff Group 4
Staff Group 5
Staff Group 6
Staff Group 7
Staff Group 8
Staff Group 9
Staff Group 10
1.7 Time Allocation per Person
ACTUAL SCENARIO
Days Days
Annual Leave (average days per person, per year) 36 36
Training (average days per person per year) 6.0 6.0
Percentage Percentage
Special Leave (average over minimum of 12 weeks) 0.00% 0.00%
Sickness Absence (average over minimum of 12 weeks) 3.00% 3.00%
Hours Hours
Supervision (average hours per person, per month) 1.5 1.5
Hours Hours
Time spent travelling (average hours per person, per week) 3.0 3.0
Meetings (average hours per person per week at all meetings, e.g. allocation, team business meetings etc) 2.5 2.5
Other e.g. projects (per person per week)
Hours Hours
Average length of allocation meeting 1.5 1.5
(please note this should also be included in total time spent in meetings)
Number Number
Average number of allocation meetings per week 2 1
Number Number
Average number of team in attendance at allocation meetings 4 4
ACTUAL SCENARIO
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6.4 Analysis
This section explains the findings in the Summary Data (see Figure 2) in terms of
actual hours and equivalent percentage of time spent on various activities, including
time lost to DNA appointments. The following sections take each area of capacity
and demand analysis in turn:
Time Spent by Activity
In Service X time spent directly with the client or patient constitutes 19.8 hours per
week or 53 % of frontline staff’s week spent directly with SUs.
The average level of sickness in Service X during the 12 week reporting period was
3%, which is lower than the NHS Scotland average level of sickness of 4%.
Time spent travelling was 7% of the working week, i.e. 2.5 hours per week for each
frontline staff member.
Annual and special leave amounted to 14% of the working week, i.e. 5.2 hours per
week per staff member.
Demand
The average weekly demand by clients for treatment and care is 2.7 WTE staff per
week including administrative time, or 2.1 WTE staff per week not including
administrative time.
DNAs
The total time lost to DNAs for first assessment appointments only in the 12 week
reporting period was 32 hours or 2.7 hours per week. Time lost to DNAs for follow-up
treatment and care appointment over the 12 week period was 203 hours or 16.9
hours per week. In total 19.6 hours per week was being lost in Service X due to
missed appointments, amounting to almost 3 days within a service of four frontline
staff (this is unlikely to include DNAs for group work).
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Capacity
The capacity of the service available for direct client work is 79.1 hours per week or
2.1 WTE staff. If we were to include administrative time, this would mean there is
capacity for 102 hours per week and 2.7 WTE staff.
Total number of staff hours spent at allocation meetings is 12 hours per week or 0.3
WTE staff.
Balancing Capacity and Demand
To balance capacity with demand and change nothing else, there is a small excess
of 3 hours per week or 0.1 WTE staff in this service - assuming all data is correct.
The figures in demand and capacity paragraphs above are equal, however the
DCAQ Tool calculations involve some rounding of figures and greater detail here in
looking at the balance between each theme.
Queue
During the 12 week reporting period there had been a high waiting time in the
Borders area and through additional staff in another part of the system the queue
was cleared and the problem rectified for the future through additional prescribing
capacity (posts were filled and there was additional nurse prescribing). However
addressing this system blockage created additional referrals seen in Service X with
26 service users waiting to be assessed.
To clear the queue of 26 people, based on the calculations of the QuEST DCAQ tool
there would need to be 237 hours or 32 days of staff time focused on this piece of
work. Bearing in mind each assessment takes 2 hours, including administrative time,
plus travel, etc. and 53% of available ‘time for direct client contact’ (from Time By
Category in Figure 2, line 2).
However the DCAQ Tool is not about giving additional resources to problem solve,
instead the approach is about finding solutions i.e. new procedures and approaches
to fix problems in the system. Once these changes have been made, additional
resources can be added to address any remaining queue. In Borders ADP the
additional prescribing capacity fixed the issues creating a queue, using existing
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budgets, and additional funds were given to NHS and local authority to clear the
waiting list speedily. The knock on effect which was not resourced is seen here in
Service X where a secondary queue developed.
System Balance
To create system balance within the service the DCAQ tool has projected that the
service should optimally receive 9 new service users each week, provide 67 follow-
up appointments per week across the service (by the 4 staff), or 7.5 follow-up
appointments for each new SU received per week. This would prevent a waiting list
developing and enable staff to meet the service user demand with the designed
assessment and follow-up programme of support.
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Figure 2 – DCAQ – Summary Data Analysis
Hours per Week % of hours per w eek
Average WTE hours per staff member 37.5 100%
Time left for Direct Client Contact 19.8 53%
Time left for Clinical/Client Admin (applying ratio from data input) 5.8 16%
Sickness Absence 1.1 3%
Time spent travelling 2.5 7%
Training 0.7 2%
Meetings (average hours per person per week at all meetings,
e.g. allocation, team business meetings etc) 2.1 6%
Supervision (average hours per person, per week) 0.3 1%
Other e.g. projects (per person per week) 0.0 0%
Annual & Special Leave 5.2 14%
Hours per week Hours per week
Your average weekly demand: Incl Clin Admin Excl Clin Admin
Your average weekly demand for first assessments: 18 9
Your average weekly demand for follow ups: 80 67
Your average weekly demand for group work: 2 2
Your average weekly demand for all client work (hours) 100 77
Your average weekly demand for all client work as WTE 2.7 2.1
TIME LOST TO DNA Total Hours Average Hrs per week
Time allocated for first assessments not used due to DNA 32 2.7
Time allocated to follow-ups not used due to DNA 203 16.9
Your average capacity per week: Hours per week WTE
Capacity available for direct client work 79.1 2.1
Capacity available for direct client work and clinical/client admin 102 2.7
Capacity spent on all other activities 34 0.9
Total no. of staff hours per week spent at allocation meeting: 12.0 0.3
Hours per week WTE
Difference between time needed and time available for direct
client work (including clinical/client admin): -2
If you change nothing else - additional hours and WTE needed
to match demand with capacity: -3 -0.1
ACTUAL SUMMARY
ACTUAL
CAPACITY
ACTUAL
DEMAND vs CAPACITY
TIME BY CATEGORY
ACTUAL
DEMANDACTUAL
ACTUAL
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Figure 2 – DCAQ – Summary Data Analysis (continued)
QUEUE
ACTUAL -
Assessment
Queue
Number of people on the waiting list 26
Total hours needed to clear queue 237
Days needed to clear queue (based on 7.5 hour day) 32
ACTUAL
Number
No of New Slots per week 9
No of F/Up Slots per week 67
Number of F/Ups for each new per week* 7.5
SYSTEM BALANCE
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7 DCAQ Projecting Hypothetical Impact
The DCAQ tool has the ability to forecast the impact of different scenarios in a
service, for example to assess the impact of increased staffing to reduce an urgent
waiting times issue, or the impact of increased group work and reduced individual
work on capacity and demand.
To project a scenario in a service, there should be a high degree of confidence in the
information entered on the data sheet in the first instance. Additionally users of the
DCAQ tool should be cautious that realistic and achievable scenarios are being set
for or by a service. Support can be provided from QuEST at Scottish Government
and potentially from National ADP Delivery Team with experience of using this tool.
Figure 3 shows the capacity available to address current demand for the service if
the service was able to make three improvements:
1. Reduce DNAs from 20% - 15% at Assessment appointments;
2. Reduce DNAs from 30% to 20% at Follow-up appointments, and;
3. Reducing the number of Allocation meetings from two to one meeting per
week.
Practically, these improvements mean exploring DNAs in the service, understanding
SUs opinions and satisfaction levels about appointment times, venues, notice
periods, methods for arranging appointments, travel requirements, travel
reimbursement, and for the assessment or intervention received. Some of this was
explored in the DNA analysis delivered by the ADP Support Team. Additionally, to
reduce the number of Allocation Meetings it would need to be possible for all (most)
staff to meet at one point in the week.
The learning gained from a DNA analysis would then need to be applied in the
service to make the necessary improvements, which should result in reduced DNAs.
Furthermore it may be possible to stop having Allocation Meetings altogether if this
takes too much time and if it is possible for the manager/team leader to allocate and
then communicate directly with individual staff about new SUs on their caseload. In
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the mental health setting for which this tool was originally developed, reducing
allocation meetings was a key area of improvement. However this was not felt to be
a beneficial option for staff in Service X due to the outreach nature of the service and
the need for staff to meet more often as a team, rather than for allocations per se.
Figure 3 – Demand vs. Capacity (Actual vs. Scenario)
0 20 40 60 80 100 120
Actual
Scenario
Hours
Demand vs Capcity (average per week)
Capacity - average hours per week available for all client work
Demand - average hours per week for all client work
Overall these three improvements would create significant increased capacity in the
service overall. This is due to less staff hours per week lost to DNAs and Allocation
meetings, as shown in Figure 4. Here the demand has reduced from the current
situation compared with the improvement scenario, due to fewer appointments being
required, since more SUs would keep their appointments. Staff capacity (available
hours) would increase in the improved scenario because staff would now have extra
hours available due to less wasted time from missed appointments.
This can be broken down in terms of Time Lost to DNAs, comparing an average of
16.9 hours lost per week currently (in excess of two days of staff time), with the
improved scenario losing an average of 9.0 hours per week (in excess of one day of
staff time).
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In the current situation capacity and demand are almost balanced, with just 0.1 WTE
extra capacity (see Figure 5), but we know there are 20-30% DNAs for
appointments. In reducing DNAs, we see that staff time is now freed up with almost
one full time post (0.9 WTE) available per week or 34 addition hours per week.
The purpose of the DCAQ tool is not about financial savings or ‘harder working’ but
about moving resources and improving practice so that staff time is freed up. This
gives service users and patients increased contact with staff, and would improve the
likelihood of greater service user outcome improvements.
Figure 4 - Time Lost by the Service Due to DNAs (Actual vs. Scenario)
TIME LOST TO DNA Total Hours Average Hrs per week Total Hours Average Hrs per week
Time allocated for first assessments not used due to DNA 32 2.7 24 2.0
Time allocated to follow-ups not used due to DNA 203 16.9 108 9.0
ACTUAL SCENARIO
Figure 5 - Overall Balance between Capacity and Demand (Actual vs. Scenario)
Hours per week WTE Hours per week WTE
Difference between time needed and time available for direct
client work (including clinical/client admin): -2 -23
If you change nothing else - additional hours and WTE needed
to match demand with capacity: -3 -0.1 -34 -0.9
SCENARIOACTUAL
DEMAND vs CAPACITY
As a continuous improvement model we would want to improve the scenario figure
once reached, but at the same time take account of the human dimension i.e. that
this is a hard-to-reach client group and we would always expect some level of DNAs.
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8 Setting Improvement Goals
The findings from the data showed that there appeared to be some capacity
available within the system, there was also a lack of consistency in appointment
lengths in similar services. It is also the case that A11 means waiting lists are
addressed in treatment services but this may not be so closely managed in other
services, with no HEAT Waiting Times compliance.
Following submission and analysis of the DCAQ, data managers from all services
were invited to attend an individual meeting with the ADP Co-ordinator and National
Advisor. This meeting was used to review the data sheets and to develop areas for
improvement. These areas for improvement were then communicated by letter with
timescales to services.
Examples of areas for improvement were:
• Confirm data accuracy
• Consider referral and engagement/DNA rates by source of referral
• Safe disengagement from one service was explored due to caseload and
funding pressures
• Agreement to explore alternative health centres sites closer to service users
• Deliver some group based work with this more stable service user group
which has as not yet been explored
• Calculate waiting times for service outwith the HEAT standard
• Consider referral and engagement/DNA rates by source of referral
• Review the process of arranging first appointment
• Patient information to be sent out with appointment letter
• Review DNA rates for assessment on a quarterly basis
• Work with referrers to raise awareness of the service and its referral criteria.
• Explore early discharge from service
• Explore alternative Service User feedback methods that support anonymity
There were some challenges to the implementation of the Improvement Letters.
While the Tool identified some areas for potential improvement and allowed some
level of estimation of capacity within the system the timing of its use before a
procurement process means that some of the learning will be built into future
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services rather than existing ones. On reflection, the Tool would be of most benefit if
used within services where there is an ongoing length to the contract.
Two of the services recently had new managers/team leaders appointed so the
services were already experiencing a process of change.
Despite the above contextual issues, progress on the improvements is reported via
the regular monitoring meetings with services.
9 Learning Points
There have been several learning points relating to use of the DCAQ Tool:
i) Size of project
One service was very small with some part-time hours allocated within a bigger
project. The DCAQ Tool was therefore not very helpful for this service as it could not
reflect where people might receive an element of support work elsewhere within the
service. The DCAQ Tool is not designed to analyse services with very small
caseloads since the findings may be unreliable.
ii) Multi-faceted roles
The Tool does not easily allow for roles which are multi-faceted where a role will be
planned to have client facing/clinical as well as development time. For example, a
Support Worker may have direct client time but may also be leading a project or
have significant time dedicated to training or capacity building. It may be that such
roles are more common in small Board areas like Borders as individual staff may
cover a variety of responsibilities. It is possible to resolve this problem relating to
development time by apportioning this separately and only using the QuEST Tool for
the remaining hours of a post.
iii) Use of available data
One service in particular was very experienced in this way of working and the data
was readily available. For others, although some data is available, it was not used
until applying the Tool locally. For example, the DATWT database contains data
25
relating to DNA rates but services (and the ADP Support Team) has not accessed
this data until 2013.
None of the service contracts at time of writing had performance indicators relating to
DNA rates. It appeared to be the case that for this reason some services felt it was
not important to collect DNA data. One service queried the ‘status’ of the data
collected by QuEST as, since it was not in the contract, how could services be held
accountable for it. However, this was still information which had been made
available to the ADP therefore it was not possible to ignore it and, given that such
information is useful, it would be helpful to use it.
iv) Balancing process and outcome measure
It is challenging to correlate activity with quality and also how to compare process
measures with outcomes measures. Within the ADP and Scottish Government it is
recognised that there is a need for a balance between reporting both on activity,
outcomes and indeed exploring the quality of interventions and SU satisfaction levels
with service provision.
10 Impact of QuEST
At the stage of writing Borders ADP is undertaking a procurement exercise for all
commissioned services. The Tool does appear to have utility for setting
improvement goals in existing services.
It has however informed local thinking and for new services which are currently being
procured monitoring and evaluation requirements will include DNA rates for all new
services, rates of planned/unplanned discharges and 12 week retention rates for
services delivering treatments. The ADP was unable to benchmark performance on
DNA and planned/unplanned discharge, however, a request to ISD has supplied
data to Borders ADP enabling benchmarking against Scotland average. At a recent
Scottish Government Drug and Alcohol Data Action Group a request was made by
the National Delivery Advisor on behalf of Borders ADP to all ADP’s to allow sharing
of this data. As a result information is expected imminently to enable benchmarking
against similar ADPs for DNAs and (un)planned discharges.
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All new Service Specifications now have a clause which says that targets may be set
and reviewed based on capacity and demand. The ADP envisages this will allow an
open two way process to look at what is a reasonable expectation of a service to
ensure quality.
A method for assessing demand and capacity has not been confirmed within the
Service Specifications.
11 Conclusion
For Borders ADP and for substance misuse services the use of the Tool was
challenging. It was done at a time of considerable uncertainty for the future and
although Service Managers were supportive of the Investment Review process it
was an unanticipated task.
The Tool has been useful. Borders ADP and substance misuse services are now
more aware of some of their demand and capacity issues. It has led onto other work
regarding barriers to access and helped improve our understanding of our alcohol
and drugs system.
Going forward the learning will continue to be of great use. As highlighted earlier, we
are building in DCAQ data into future monitoring alongside outcomes reporting.
Borders ADP believes this will allow us to more accurately support service
improvement and evaluate our investment profile.
We are very interested to explore further the benchmarking data which ISD is
developing and this will allow us to monitor our local performance.
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References
Doig, F., Walker, S. (2013) ADP Investment Review – Findings and Recommendations: Report from ADP Executive Group; Doig, F. (2013) Borders Alcohol & Drugs Partnership (ADP): Future Model of Investment.
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Appendix 1 – Mental Health DCAQ Tool
QuEST Mental Health: DCAQ Tool
[Enter Team & Date here]
DEMAND1.1 Time Period for Demand Data
Enter number of weeks you wish to calculate Demand and for (recommended minimum of 12 weeks)
1.2 Referral Information - Please enter the data for the time period specified above
ACTUAL SCENARIONumber Number
Enter number of referrals received in time period
Enter number of opt-outs in time period
Enter number of people referred elsewhere as inappropriate for your service in time period
Enter number of individuals still on waiting list for assessment at end of snapshot time period
Select Select
Does your service run a separate waiting list for treatment?
If yes, enter number of individuals on waiting list for treatment at end of snapshot time period
1.3 Did Not Attend (DNA)
Number Number
DNA rate for 1st assessment slots
calculate as (no. of 1st assessment slots missed/ no. 1st assessment slots offered)*100
DNA rate for individual follow-up slots
calculate as (no. of follow-up slots missed/ no. of follow up slots planned)*100
1.4 Group Work
Select Select
Does your service do group work?
If you selected No, please go to section 1.5
If you selected Yes, please fill in the following:
Number Number
Percentage of people who only go into individual work
Percentage of people who only go into group work
Percentage of people who receive both group and individual work
Percentages should total 100%, if not, please check your figures ----------- check total: 0% 0%
Average number of sessions per group intervention
Average number of people per group session
Average number of staff per group session
1.5 Slot length, Clinical/Client Admin, and Follow-up
Slot length & Associated Clinical AdminMinutes Minutes
Average clinical/client contact time taken per first assessment
Average clinical/client admin time taken per first assessment
Average clinical/client contact time taken per follow-up
Average clinical/client admin time taken per follow-up
Average clinical/client contact time taken per staff member per group session
Average clinical/client admin time taken per staff member per group session
Follow-Up
Number Number
Average number of follow-up visits per client
Sections 1.3 - 1.5 require you to enter a number of variables in order to calculate the time
needed to respond to the information you have entered above. Use data that best reflects what
usually happens in your service. For guidance and suggestions on how to calculate a
representative value for each variable, contact QuEST via the details provided on the Introduction
worksheet.
Fill scenario cells with actual data
Hide Scenario Cells
Unhide Scenario Empty scenario
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Appendix 1 – Mental Health DCAQ Tool
CAPACITY1.6 Staff Allocation - Per Week
Staff Group <please specify below> Staff WTE
Hours
per WTE Staff WTE
Hours per
WTE
Staff Group 1 37.5 37.5
Staff Group 2 37.5 37.5
Staff Group 3 37.5 37.5
Staff Group 4 37.5 37.5
Staff Group 5 37.5 37.5
Staff Group 6 37.5 37.5
Staff Group 7 37.5 37.5
Staff Group 8 37.5 37.5
Staff Group 9 37.5 37.5
Staff Group 10 37.5 37.5
1.7 Time Allocation per Person
ACTUAL SCENARIO
Days Days
Annual Leave (average days per person, per year)
Training (average days per person per year)
Percentage Percentage
Special Leave (average over minimum of 12 weeks)
Sickness Absence (average over minimum of 12 weeks)
Hours Hours
Supervision (average hours per person, per month)
Time spent travelling (average hours per person, per week)
Meetings (average hours per person per week at all meetings, e.g. allocation, team business meetings etc)
Other e.g. projects (per person per week)
Hours Hours
Average length of allocation meeting
(please note this should also be included in total time spent in meetings)
Number Number
Average number of allocation meetings per week
Number Number
Average number of team in attendance at allocation meetings
ACTUAL SCENARIO