Andrew Paul Fennell1,2, Matthew Frank Hunter3,4, Gregory Philip Corboy5,6,7. 1. Monash Genetics, Monash Health, Melbourne, VIC, Australia. andrew.fennell@monashhealth.org. 2. Department of Paediatrics, Monash University, Melbourne, VIC, Australia. andrew.fennell@monashhealth.org. 3. Monash Genetics, Monash Health, Melbourne, VIC, Australia. 4. Department of Paediatrics, Monash University, Melbourne, VIC, Australia. 5. Department of Pathology, Monash Health, Melbourne, VIC, Australia. 6. School of Clinical Sciences, Monash University, Melbourne, VIC, Australia. 7. Department of Clinical Pathology, The University of Melbourne, Melbourne, VIC, Australia.
Abstract
PURPOSE: Clinical genetics is an evolving specialty impacted by the availability of increasingly sophisticated investigational technologies. Methods for monitoring the changes in workload and workflow are necessary to ensure adequate service resourcing. METHODS: A literature search of known workload and workflow studies was completed, identifying metrics of value. A framework of metrics to allow consistent capture in clinical genetics practice was developed. This framework was then applied to local general genetics service data to evaluate recent changes in service delivery. RESULTS: Literature regarding service delivery metrics in clinical genetics services is limited and inconsistent in application. The metric framework generated is a useful tool for consistent and ongoing evaluation of general genetics services. Through application of the framework, new service delivery trends and significant changes in workload were identified. CONCLUSION: Studies of clinical genetics service delivery suffer from the use of inconsistent metrics. This framework will allow for monitoring of changes to service delivery, caseload volume, caseload complexity, and workforce over time. Local data presented demonstrate the significant effect that implementing clinical genomic sequencing has had on clinical service delivery. Applying this framework produces a comprehensive service characterization, enabling funding bodies to justify resourcing that addresses the growing demand of clinical genetics.
PURPOSE: Clinical genetics is an evolving specialty impacted by the availability of increasingly sophisticated investigational technologies. Methods for monitoring the changes in workload and workflow are necessary to ensure adequate service resourcing. METHODS: A literature search of known workload and workflow studies was completed, identifying metrics of value. A framework of metrics to allow consistent capture in clinical genetics practice was developed. This framework was then applied to local general genetics service data to evaluate recent changes in service delivery. RESULTS: Literature regarding service delivery metrics in clinical genetics services is limited and inconsistent in application. The metric framework generated is a useful tool for consistent and ongoing evaluation of general genetics services. Through application of the framework, new service delivery trends and significant changes in workload were identified. CONCLUSION: Studies of clinical genetics service delivery suffer from the use of inconsistent metrics. This framework will allow for monitoring of changes to service delivery, caseload volume, caseload complexity, and workforce over time. Local data presented demonstrate the significant effect that implementing clinical genomic sequencing has had on clinical service delivery. Applying this framework produces a comprehensive service characterization, enabling funding bodies to justify resourcing that addresses the growing demand of clinical genetics.
Entities:
Keywords:
genetics; metrics; service delivery; workforce
Authors: Jacqueline D Hunter; Eden G Robertson; Kate Hetherington; David S Ziegler; Glenn M Marshall; Judy Kirk; Jonathan M Marron; Avram E Denburg; Kristine Barlow-Stewart; Meera Warby; Katherine M Tucker; Brittany M Lee; Tracey A O'Brien; Claire E Wakefield Journal: J Pers Med Date: 2022-08-18
Authors: Heidi Cope; Rebecca Spillmann; Jill A Rosenfeld; Elly Brokamp; Rebecca Signer; Kelly Schoch; Emily G Kelley; Jennifer A Sullivan; Ellen Macnamara; Sharyn Lincoln; Katie Golden-Grant; James P Orengo; Gary Clark; Lindsay C Burrage; Jennifer E Posey; Jaya Punetha; Amy Robertson; Joy Cogan; John A Phillips; Julian Martinez-Agosto; Vandana Shashi Journal: Mol Genet Genomic Med Date: 2020-07-30 Impact factor: 2.183