22/03/2018 PenCHORD Timeline THE THE RO ROLE OF OF SYSTEM SYSTEM DY DYNAMICS IN IN HE HEAL ALTH TH AND AND SOC SOCIAL CARE CARE 2009 2010 2011 2012 2013 2014 2015 2016 2017 Launch PenCLAHRC 2 PenCLAHRC Martin Pitt Event starts Training Modules Setting up organisation Associate Professor of Healthcare Modelling and Simulation Seminar /Showcase Series University of Exeter : Medical School HSMA programme 70 Collaborative Project work Number of completed projects Mast House, Plymouth : 13 March 2018 SW CLAHRC – Collaboration for Leadership in Health Research and Care Presentation structure • Background to PenCHORD/PenCLAHRC • The increasing use of SD in healthcare modelling • Some examples of SD application in healthcare • Areas of application that SD is best suited • Reasons for the increasing adoption of SD • Main Limitations • Comparison with other modelling approaches • Q&A PenCHORD Needs http://clahrc ‐ peninsula.nihr.ac.uk/penchord Data Resources • Peninsula • Collaboration for • Health • Operational OR: Many relevant approaches/tools. • Research and Solutions/tools • Development Guidance/Collaboration Empowerment/Learning PenCHORD 1
22/03/2018 Increasing use of SD in healthcare 1. Mapping Acute Patient Flows ‐ impact • The specific proposals for improving patient experience were fed back into the DoH and, hence, the NHS. This work was presented to Emergency Services Action Team (ESAT), reporting on both the specific results and the general mapping approach. The report itself, in the words of one of the Steering Committee members, ‘has informed the modernisation of [A&E] services initially through an [A&E] task force and subsequently through the Modernisation agency.’ • Another Committee member completes the story of the project described in this paper: ‘In policy terms, [the] work made an important contribution to work on improving A&E departments. [The] report showed how relatively simple modifications in physical arrangements and treatment pathways might improve the delivery of service ... [the] report also helped to inform the work of the Accident and Emergency Modernisation Programme. Health SD publications by year (from Brailsford et al. 2008) 1. Mapping Acute Patient Flows 2. Dentistry Workforce planning in Sri Lanka Workshops Preliminary consultations Lane, D.C. & Husemann, E. (2008). System dynamics mapping of acute patient flows. Journal of the De Silva MDK. 2012. Dental Workforce Planning for Sri Lanka. PhD Thesis conferred by Univ. of Southampton Operational Research Society, 59(2), pp.213–225. 1. Mapping Acute Patient Flows ‐ Methods 2. Dentistry Workforce planning in Sri Lanka 2
22/03/2018 2. Dentistry Workforce planning ‐ impact 3. Emer Emergency ncy and and on on ‐ demand demand health health ca care • By illustrating various policy options with the model simulation for 15 years, the author was able to convince the Ministry of Higher Education and the Ministry of Health, that if • Outputs the status quo continues, the dental workforce supply dynamics over the next 15 years will be almost the same as today. There will be around 250 dental surgeons seeking • Projections from the model suggested that if emergency admissions continued to rise at government employment throughout the simulation period from 2010 to 2025. the rates experienced, average bed occupancy levels would be unmanageably high Furthermore, although the waiting time to secure government employment will improve, within two to three years. The most promising intervention was found to be the it will be more than three and a half years, throughout the above period. diversion of selected elderly patients to specialist investigation centres. • Based on this model illustration, the Ministry of Higher Education decided not to increase • Impact the intake of dentistry students for the next ten years. Moreover, the Ministry of Health • The model was used to investigate patient flows and bottlenecks and as a tool for also accepted the study results and was convinced, based on the advice and provoking and facilitating discussion. The Nottingham steering group for ECOD used the recommendations of the author, of the long ‐ term adverse consequences of having model to test and evaluate different scenarios of care. However, the primary use of the unemployed dental surgeons. model was for promoting greater understanding of the dynamics of the system rather • The Ministry decided to obtain special Cabinet approval to create 400 additional than in generating numerical outputs. vacancies in the Ministry of Health within the next three years. With the implementation • The essentially generic framework adopted and the model’s use of routinely collected of this proposal, most of the country ‟ s dental treatment needs could be met, while data means that this approach can readily be adapted elsewhere. harnessing the potential of trained dental manpower in a productive manner to enhance the country ‟ s health development. 3. Emer Emergency ncy and and on on ‐ demand demand health health ca care 4. Fa Factors tha that influence uence ambul bulance ance deman demand ISSUES: Emergency Care On Demand (ECOD) Literature project, focused on the at the time • Daily, Weekly, Monthly and Seasonal Search increasing pressure on emergency medical Fluctuations well understood service use and consequent increasing • Increasing year ‐ on ‐ year demand for Literature number of hospital admissions in Review ambulance services Nottingham. The project posed four main research • Recent changes (e.g. introduction of 111 Build Influence questions, based around identifying the Service) affecting demand, but annual Map current configuration of the system, more increases before this precisely defining the present level of Discuss and demand, how the system could be • Studies typically look at A & E demand Amend Map developed, and to what extent community with ambulance as one factor preferences were driving use of Accident Agree Final Map • Potential national application and Emergency (A&E). Brailsford S, Lattimer VA, Tarnaras P, Turnbull C. Emergency and on ‐ demand health care: Modelling a large complex system. J Oper Res Soc 2004; 55:34 ‐ 42 3. Emer Emergency ncy and and on on ‐ demand demand health health ca care 4. 4. Fa Factors tha that influence uence amb ambulan ance deman demand Sys System em Dynam Dynamics cs Model Model Distal Cause 1 Proximal Cause 1 Proximal Cause 2 People who People who have not yet have called an called an ambulance People calling for an ambulance ambulance 3
22/03/2018 4. 4. Fa Factors tha that influence uence amb ambulan ance deman demand Qualitative and Quantitative dimensions The The Re Results • SD has two basic modes of operations: • An essentially descriptive mode, qualitative modelling , in Absolute Magnitude of a similar fashion as problem structuring methods (Soft OR Rank Influencing Factor Difference (%) 1 Number of people with regular falls per month 34.3122% modelling), where the focus is on changing behaviour 2 Initial Elderly Population Size 28.8402% through the participation of the client on the definition of 3 Proportion of elderly population limited a lot by disability 26.3619% 4 Proportion of limited a lot by disability who regularly fall 26.3619% the model, measurement of the changes in behaviour during the project and representation of bounded 5 Proportion of pop who are users of adult mental health services 21.2111% 6 Number of adult mental health ambulance users per month 21.2111% rational decision makers in their models. 7 Number of calls referred from 111 Service per month 10.5310% • A predictive/prescriptive mode 8 Proportion of elderly population limited a little by disability 7.9503% 9 Proportion of limited a little by disability who regularly fall 7.9503% 10 Proportion of population using Minor Injury Units per month 5.6803% Sources of Information for SD Modelling 4. Fa 4. Factors tha that influence uence amb ambulan ance deman demand Mental data bases SD focus FINDINGS Observation and experience • The prevalence of regular falls amongst the elderly may be having a significant impact on the number of ambulance calls being made • Further investment in falls prevention strategies could lead to Written data base significant reductions in ambulance calls • Strategies to reduce the higher propensity of those with mental Traditional focus health problems to use ambulances could also help Numerical data base • Referrals from the 111 Service and Minor Injury Units are contributing non ‐ trivially to ambulance demand Based on Forrester 1994 An example of SD using a soft (qualitative) Reasons for the increasing adoption of SD perspective Key Benefits and Advantages of SD • Interviews generated a set of different textual explanations of the business processes. • Combined Qualitative and Quantitative dimensions • Analyzed them together to find similarities in concepts and causal linkages. • System based perspective • One remarkable insight from the CLD and the verbal description of the feedback • Simplicity and level of abstraction processes was the existence of a number of soft, hard ‐ to ‐ measure, variables. • Low data requirement • While there would be opportunity to collect data if there was enough time (a year?) and resources, it was valuable to understand the dynamics of the business • Ability to capture soft/behavioural factors in a short period of time. • Easy accessibility and quick to run • Software support 4
Recommend
More recommend
