MH Tariff Completion & Accuracy Project lead: Bailey Mitchell - - PowerPoint PPT Presentation
MH Tariff Completion & Accuracy Project lead: Bailey Mitchell & Auzewell Chitewe Project team: Peter MacRae, Stevie Jay Cavanaugh, Luke Mearns, Fatema Ibrahimi, Victoria Cohen, Elaine Athanas, Shazia Hashmi, Mark Dunne, Helen
Project lead: Bailey Mitchell & Auzewell Chitewe Project team: Peter MacRae, Stevie Jay Cavanaugh, Luke Mearns, Fatema Ibrahimi, Victoria Cohen, Elaine Athanas, Shazia Hashmi, Mark Dunne, Helen Chinyere Project sponsor: David Bridle
Allocating all service users to care clusters in a timely and accurate manner is essential for the Trust to maintain financial viability going forward. It is also important in determining the effectiveness of the care we provide and in benchmarking across services and providers. We identified a clear need and room for improvement.
Following a significant change in process and service redesign the previous method of clustering was no longer sustainable. Expired and Awaiting Clusters increased well beyond agreed thresholds and a solution was urgently needed.
To have no more that 5% expired or awaiting cluster cases by 1st of April 2015. Further, to increase accuracy of clustering to a minimum of 80% by April 2016.
Complete & Accurate Care Clustering Knowledge Training
Changes Targeted Training, speficic to team environment Consultant specific training Engagement of qualified staff in HoNos scoring and cluster recommendations Skills Variation Ensure basic RiO training and computer skills training for all staff Systems in Place Right Information, Right Time Local engagement with RS. Local team level process to prompt clustering. Bi-monthly and then monthly catch all reporting from Q&P Simplified process regardless of environment Development of ward and community specific processes Time Ensure develped processes are efficient Data Accuracy MH Tariff Dashboard Ensure RS dashbaord presents the correct data in a consumable fashion Attitude Centralised system removes
(relationship to clinical work non-existant) Tranisition from central
clustering as part of the clinical process Belief regarding the likelyhood or validity of the system going forward Promote importance of MH Tariff use and its connection to funding streams.
A P S D A P S D Cycle 1: Transition from a local manual process to the creation of a more reliable centralised fit for purpose tool Cycle 2: Familiarise teams with new tool and modify tool following feedback Cycle 4: Community and inpatient engagement – different strategies needed Cycle 3: Test clustering & RiO recording intervals with admin and clinicians
Cycle 5: “How to record clustering information in RiO” training video
0% 2% 4% 6% 8% 10% 12% 14% 16% 18% 20% Apr-14 May-14 Jun-14 Jul-14 Aug-14 Sep-14 Oct-14 Nov-14 Dec-14 Jan-15 Feb-15 Mar-15
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Median Measure
Clustering Tool Design Clustering Tool Implementation
0% 1% 2% 3% 4% 5% 6% Apr-14 May-14 Jun-14 Jul-14 Aug-14 Sep-14 Oct-14 Nov-14 Dec-14 Jan-15 Feb-15 Mar-15
Run Chart
Median Measure
Service Redesign Clustering Tool Design Clustering Tool Implementation
What did we learn? New ways to interpret data. Local DMT level ownership of QI was likely a major contributor to the significance of the improvement. Engagement at all levels of the process has improved and the changes put in place allowed teams to take more direct responsibility for their own data. One of the benefits of the new process is the permission it gives for you to fail. 80/20 rule – by focusing on the teams with the largest potential impact, greater global gains were achieved. Challenges: Assuring senior stakeholders that improvement would be made but that QI takes time. Building the will amongst staff that have doubts about the value of Clustering as well as QI methodology. Sustaining the gains achieved – though results were maintained for a significant period of time, when the reporting services tool suffered down time during the RiO merge performance dropped.