A new tool for the assessment of resilience in healthcare: a social network analysis perspective

Tarcisio A. Saurin, Vanessa B. Becker

8th RHCN meeting, Osaka, August 2019

Collaboration relies on social networks, and these influence the four resilience abilities

Anticipating, monitoring, responding, learning

These can be assessed in terms of social networks

Broader research question

How can SNA support the assessment of resilience in healthcare?

Background

Who are the key players in the four networks?

Are the key players available?

Are the key players the same in the four networks investigated?

Do the key players provide reliable information?

How can SNA contribute to the assessment of resilience in socio-

technical systems?

Anticipating Monitoring

Responding Learning

Which actors are the key sources of resilience?

Research question addressed in this presentation

(availability . reliability . betwenness* . in-degree*) * Normalized

• Maximum possible resilience score = 5 . 5 . 5 . 5 = 625

The score can be calculated for each agent within each network

TENTATIVE Resilience Score

(1) Characterization of the respondent and of three contextual factors

Frequency of interruptions

Participation in daily interdisciplinary rounds

Shift

(2) A roster of staff, from which the respondent should select those they rely upon for advice. Besides, questions on:

Availability (time)

Reliability (precision)

(3) Contribution of the interaction for each resilience ability

Frequency of the interaction

The SNA questionnaire

Monitoring consists of continually perceiving changes, disturbances, threats or opportunities during your daily activities, as close as possible to real-time

E.g. You may be interested in monitoring relevant changes in the patient´s clinical condition

For the people you selected, please indicate the frequency they are contacted for monitoring

Exemplar question: monitoring

Never Less than once a month

1-3 times a month

1-3 times a week

Daily

Nurse A X

Doctor B X

Adult ICU of a major public teaching hospital, 34 beds

Patients admitted from Emergency department, surgical unit, wards, other hospitals

About 200 employees from 15 professional groups, 6 shifts

Scenario of this study

Overall response rate = 67%

Sample profile

n % n %

Doctors 40 20% 16 12%

Nurses 32 16% 25 19%

Nurse technicians 115 57% 84 63%

Staff Total Respondents

n % n %

Doctors 40 20% 16 12%

Nurses 32 16% 25 19%

Nurse technicians 115 57% 84 63%

Allied health 14 7% 8 6%

TOTAL 201 100% 133 100%

Staff Total Respondents

Never Rarely Sometimes Frequently Always

Participation in rounds

35% 10% 24% 18% 14%

Frequency of interruptions

3% 14% 27% 45% 11%

Results

Metrics Monitoring Anticipating Responding Learning

Density 2.2% 2.0% 2.8% 2.3%

Connectedness 46% 54% 64% 55%

Highest-in-degree DR 169 N135 DR 169 DR 169

Highest-out-degree NT 10 NT 106 NT 32 N186

Betweeness N94 N94 N135 N94

The ranking of several agents was VERY DIFFERENT across the networks, based on the resilience score

NT 193 was the 31st for Monitoring, and the 6th for Anticipating

DR 48 was the 41st for Responding and the 4th for Learning

Other agents had more stable and higher positions

N94 was the 1st for Monitoring, Anticipating, and Responding, while the 2nd for Responding

A same interaction can effectively contribute to the four potentials

How can these professionals balance availability, reliability, betwenness, and in-degree? Which resilience strategies?

Results

Doctors were slightly less frequent in the top 10 resilience scores, in comparison with considering only the in-degrees

They appeared 16 times among the top ten based on the in-

degrees rankings

12 times based on the resilience scores

Doctors were less available than other professionals

How can availability increase?

Results

Low density may suggest reliance on other resources for resilience

It may also simply be that the four potentials are weak in the ICU

Application of RAG

Captured social interactions are those when the respondent requests advice

What about when the respondent offers advice?

E.g. A nurse may take the initiative to let a doctor know what is

going on in the ICU (monitoring)

Some implications and limitations

Complete data analysis

Interviews with the main sources of resilience

Further statistical analysis (clusters)

Combine the multiple network layers

Additional test: new ICU, and other ICUs (other countries?)

What a resilient network looks like?

What are normal thresholds for the network metrics?

Investigation of how visual management influences on the networks

Next steps

Thank you!

Doctors are usually the main source of advice for other professionals

Doctors usually request advice from other doctors (54% density for the learning network)

Density for the learning network

* In degree: How much they are contacted by other professionals; ** Out-degree: how much they contact others

Main results

Doctor* Nurse Nurse technician

Allied health Average out-degree (without

own group)

Doctor** 0.54 0.29 0.31 0.18 0.26

Nurse 0.58 0.47 0.30 0.33 0.40

Nurse technician 0.43 0.46 0.36 0.28 0.39

Allied health 0.66 0.41 0.35 0.64 0.47

Average in-degree (without own group)

0.56 0.39 0.32 0.26

23 questions on the resilience potentials

Scale from fully disagree (1) to fully agree (5)

Ex. 1. The interdisciplinary rounds contribute to the monitoring of what is going on in the ICU, including conditions and events that may imply in

undesired impacts to the ICU and clinical condition of patients

Ex. 2 Interruptions in my work (telephone, colleagues, family, etc.) are not frequent and not hinder the monitoring of what is going on in the ICU

Three closure questions

The ICU is resilient

Patients are safe

Professionals are safe

Complementary RAG survey