Applying system dynamics to health and social care commissioning in - PowerPoint PPT Presentation

Applying system dynamics to health and social care commissioning in the UK Professor Eric Wolstenholme, David Monk, Gill Smith & Douglas McKelvie, OLM Consulting Coping but not coping – masking the reality of running organisations beyond their design capacity

What we have been doing Modelling Patient Pathways for Older People Across Health and Social Care – National and Local Modelling Disease Progression and Mental Health Services

Aims To demonstrate the benefits of applying systemic polices across long patient pathways involving multiple health and social care agencies

Whole systems pathway modelling in health and social care fas t s tr eam medic al … medic al em ergenc ies medical medical nur s ing - r es idential beds (fast) emergencies nursing residential medic al beds interm ediate c are medical domic illiar y c ar e pr imar y c ar e hos pital as s es m enttr … beds slow intermediate domicilliary hospital primary care care care assessment tr ans fer s from medic … transfers medical to surgical nhs c ontinuing c are NHS continuing elec tiv e w ait lis t care s ur gic al beds elective surgical beds wait list Post Acute Care hos pital av oidenc e s ur gic al emer genc ies hospital surgical avoidance emergencies Routes to home exist from Primary Care Acute Care Intermediate Care each sector

Modelling patient pathways for older people • Delayed hospital discharges • Investment in new capacity • Reducing elective wait times • Increasing elective episodes • Allocating community beds • Improving assessment efficiency

What is it we provide? What might be: • Systemic answers? • Insights? • Learning? What is: • Validation – addressing inconsistencies • Surfacing of hidden policies? Implications for: • Health and social care practice • The meaning of data • System dynamics modelling

OLM SD is unique in its ability to draw out inconsistencies Organisation Actual structure Actual behaviour Actual Actual Actual Data over time Policies Processes Management Perceived structure Perceived World Perceived Perceived Perceived behaviour Data Processes Policies over time Model structure Model Processes behaviour Data used in Policies used used in over time models in models models Modelling World Mismatches in Data/Structure inconsistencies behaviour and revisions

Findings • Gaps in data • Inconsistencies in data • No bottlenecks where we might expect to see bottlenecks

Hypothesis from findings? Hypothesis: Health and social care organisations: 1. have to meet demand and political performance targets for their services, irrespective of their supply capabilities and so have to work beyond their design capacity 2. have informal, well-intended coping actions to achieve performance targets 3. these underlying actions mask the ‘over capacity’ situation and have serious unintended consequences – future time bombs

Example of inconsistency in data - capacity modelling community care purchaser care supplier 200 patients 20 capacity spare capacity patients/day of service demand 20 P patients /day await service service discharge rate service admission rate start service receive service rate 20 2 00 patients/day patients 20 200 patients/day patients For structure/data consistency: length of service Service discharge rate = receive service / length of service 10 days BUT often find: Service discharge rate not equal to receive service / length of service AND no bottlenecks where we might expect bottlenecks Structure wrong or data wrong or both???

In some circumstances there is no such thing as a capacity constraint Sometimes waiting has to be minimal (emergencies, government performance measures) Design capacity has to be exceeded Informal structures come into play Informal structures creates their own data

Surfacing of alternative structure community care purchaser care s upplier P3 capac ity spare capacity P2 of service demand await s ervic e service dis charge rate service admission rate start s ervic e receiving s ervic e rate unmet need P1 diversion rate to other services length of service P4

Implications for health and social care practice

Examples of coping strategies for health and social care and their unintended consequences Changes in gate keeping thresholds in primary care: Intended outcome: to reduce demand, Unintended outcomes: • demand absorbed by stocks outside the health and social care system • creation of cumulative unmet need • responsibilities for care pushed back on families, charities and communities. • ultimately kick-back on services, with a higher proportion of people entering as emergencies

Examples of coping strategies for health and social care and their unintended consequences Reducing lengths of stays in acute hospitals: Intended outcome: creation of spare capacity Unintended outcomes: • creates more incomplete episodes of care and increases chance of readmissions. • increases the ‘revolving door phenomena’, a small population of people recycle continuously through hospital and become a significant problem.

Examples of coping strategies for health and social care and their unintended consequences Institutionalising the practice of outliers in hospitals: Intended outcome: minimise A and E wait time. Unintended outcomes: • disruptive bed shifts for patients • inefficiencies for medical staff - locating patients.

Examples of coping strategies for health and social care and their unintended consequences Rationing home help hours in domiciliary care: intended outcome: creation of capacity in post acute Unintended consequence: • patient dissatisfaction • high cost intervention • corrupts future investment plans.

Conclusions for health and social care and their unintended consequences All these actions are well known in each agency along the patient pathway for older people So what has our work done: • provided a non-career limiting forum for talking about coping actions • documented the extent of the use of these mechanisms • surfaced the unintended consequences of such actions – deferment of problems through time • suggested that these actions be addressed if sustainable change is the aim

Implications for the meaning of data Current ethos: 1. Data is absolute 2. More data is better 3. Data is the source of evidence-based thinking used for the purpose of organisational review and change management. Hypothesis for data here: data is a reflection of management actions, not a characteristics of the entities measured. E.g. data collected on lengths of service during periods of coping : • reflects nothing more than management overload • bears no mathematical relationship to other parameters or to patient characteristics.

Does data create action OR does action create data??

implications for system dynamics modelling 1. System dynamics is one of the few ways to uncover inconsistencies and informal policies. 2. Informal actions are a major source of hidden feedback in organisations 3. ‘What is’ analysis: When validating models against past data we must know the process/policy structure that existed at both the formal and informal levels

implications for system dynamics modelling 1. ‘What might be’ analysis: the first stage here should be to expose the real unmasked behaviour of the system when coping policies are withdrawn. 2. Only then is it sensible to try to demonstrate the effects of systemic policies to redesign the system. 3. The ‘what might be’ phase of system dynamics studies requires ‘best practice’ data. Not past data associated with past practice, which we wish to replace.

Does data validate a model OR does a model validate data??

Data driving actions OR actions driving data • • • • •