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Implementing an mHealth triage intervention for health care workers at primary health centres in urban Blantyre, Malawi - a pilot study
Implementing an mHealth triage intervention for health care workers at primary health centres in urban Blantyre, Malawi - a pilot study
Implementing an mHealth triage intervention for health care workers at primary health centres in urban Blantyre, Malawi - a pilot study
Implementing an mHealth triage intervention for health care workers at primary health centres in urban Blantyre, Malawi - a pilot study
Implementing an mHealth triage intervention for health care workers at primary health centres in urban Blantyre, Malawi - a pilot study
Implementing an mHealth triage intervention for health care workers at primary health centres in urban Blantyre, Malawi - a pilot study
Implementing an mHealth triage intervention for health care workers at primary health centres in urban Blantyre, Malawi - a pilot study
Implementing an mHealth triage intervention for health care workers at primary health centres in urban Blantyre, Malawi - a pilot study
Implementing an mHealth triage intervention for health care workers at primary health centres in urban Blantyre, Malawi - a pilot study
Implementing an mHealth triage intervention for health care workers at primary health centres in urban Blantyre, Malawi - a pilot study
Implementing an mHealth triage intervention for health care workers at primary health centres in urban Blantyre, Malawi - a pilot study
Implementing an mHealth triage intervention for health care workers at primary health centres in urban Blantyre, Malawi - a pilot study
Implementing an mHealth triage intervention for health care workers at primary health centres in urban Blantyre, Malawi - a pilot study
Implementing an mHealth triage intervention for health care workers at primary health centres in urban Blantyre, Malawi - a pilot study
Implementing an mHealth triage intervention for health care workers at primary health centres in urban Blantyre, Malawi - a pilot study
Implementing an mHealth triage intervention for health care workers at primary health centres in urban Blantyre, Malawi - a pilot study
Implementing an mHealth triage intervention for health care workers at primary health centres in urban Blantyre, Malawi - a pilot study
Implementing an mHealth triage intervention for health care workers at primary health centres in urban Blantyre, Malawi - a pilot study
Implementing an mHealth triage intervention for health care workers at primary health centres in urban Blantyre, Malawi - a pilot study
Implementing an mHealth triage intervention for health care workers at primary health centres in urban Blantyre, Malawi - a pilot study
Implementing an mHealth triage intervention for health care workers at primary health centres in urban Blantyre, Malawi - a pilot study
Implementing an mHealth triage intervention for health care workers at primary health centres in urban Blantyre, Malawi - a pilot study
Implementing an mHealth triage intervention for health care workers at primary health centres in urban Blantyre, Malawi - a pilot study
Implementing an mHealth triage intervention for health care workers at primary health centres in urban Blantyre, Malawi - a pilot study
Implementing an mHealth triage intervention for health care workers at primary health centres in urban Blantyre, Malawi - a pilot study
Implementing an mHealth triage intervention for health care workers at primary health centres in urban Blantyre, Malawi - a pilot study
Implementing an mHealth triage intervention for health care workers at primary health centres in urban Blantyre, Malawi - a pilot study
Implementing an mHealth triage intervention for health care workers at primary health centres in urban Blantyre, Malawi - a pilot study
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Implementing an mHealth triage intervention for health care workers at primary health centres in urban Blantyre, Malawi - a pilot study

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Dr Nicola Desmond's presentation at Meningitis Research Foundation's 2013 conference, Meningitis & Septicaemia in Children & Adults

Dr Nicola Desmond's presentation at Meningitis Research Foundation's 2013 conference, Meningitis & Septicaemia in Children & Adults

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  • Hi my name is Nicola Desmond. I’m a medical anthropologist, and Wellcome Trust Fellow based at the MLW MOP in Blantyre Malawi. We have been working in collaboration with the Meningitis Research Foundation to develop a pilot study implementing an mHealth triage intervention for health care workers at primary health clinics. This study is part of the MRFs expansion of its focus into tackling meningitis in resource-poor settings – and is known as the AcMen project. Malawi has made significant progress in reducing deaths in children aged under five but remains with an infant mortality rate of 112 per 1000 live births. The major causes of infant deaths are pneumonia (23%), malnutrition (22%), diarrhoeal diseases (18%) and malaria (14%).
  • So to begin I want to introduce you to Edith Cheonga who lives in a village outside of Blantyre with her young parents and elder brother, Ronald.Following a fever lasting three days Edith, then aged 3, was taken by her father to the nearest primary health centre. The clinician at the health centre conducted a malaria test and although this showed neagtive, prescribed her with a local malaria treatment, LA and sent her home. Despite giving her the prescribed medication Edith’s fever continued and three days later she was crying incessantly and developed a bulging fontanel. Her parents returned to the health centre with her and after insistence from her father was referred to Queen Elizabeth Central Referral Hospital in Blantyre. She was diagnosed with meningitis and remained in hospital for ten days. After returning home Edith continued to cry a lot and her parents returned to Queens with her. Fluids were found on her brain and she was operated on.Edith is now 4 and can no longer see, hear or sit up properly.Why am I telling you about the story of Edith?
  • Edith is a case study from research carried out by MLW in 2010 into health seeking pathways which was based on the fact that late presentation is a driving factor for death from bacterial meningitis and other severe illnesses where prompt treatment is essential to effective management.
  • From this initial research we identified a number of factors that impact at both community and primary health level on the recognition of meningitis and the capacity to do something about it – those coloured green reflect community level factors, those coloured blue represent primary health level factors and those coloured yellow represent problems in recognition that occur at both community and primary health levels.
  • So we knew that something was needed to be done at both community and primary health level. In this presentation I focus on our response to factors impacting on recognition of ABM and other severe illness amongst paediatrics at primary level. These areUnsystematic and informal triage where adults and children sit together and are seen on a first come first served basis rather than prioritised systematically. High numbers of patients seen on a daily basis in busy PHCs which creates additional burdens on healthcare workers, further exacerbating an erratic consultation system. Malaria season exacerbates primary clinic burdens from December to March. And finally high levels of misdiagnoses for sever illness.
  • So we wanted to explore the feasibility of implementing a triage system within PHCs through the use of mHealth technologiesWe had four specific objectives for this pilot study:To develop an mHealth algorithm based on WHO approved ETAT To implement a prioritisation system using this mHealth triage algorithmWhich we hoped to encourage appropriate referral decisions and track referrals to Queen Elizabeth Central HospitalAnd then to evaluate triage system using mixed method approaches
  • Our intervention is adapted from the ETAT protocol. This stands for Emergency Triage Assessment and Treatment and was developed for tertiary settings to identify and respond to severe illness as a component of the Integrated management of childhood Illneses or IMCI protocol.Our focus was on the triage component which facilitates the early identification of children with immediately life-threatening conditions based on identification of a few important clinical signs and the ability to identify negative symptoms quickly.The algorithm is simple with easy to follow guidelines appropriate for application by all cadres of staff and requiring limited clinical training. It is also easy to conduct when patients are queuing.
  • Recognising the need for consistent application of the triage algorithm we decided to explore the use of mobile health technologies. Mobile health or mhealth is the practice of public health and medicine supported by mobile devices. Phones have been generally used for data collection especially in hard to reach areas or as point of care tools including the provision of clinical algorithms. Through the use of active prompts consistent across all cases mHealth has the capacity to improve diagnosis and act as a training and monitoring tool.
  • So now I want to describe the intervention we developed which is the first time mHealth has been used for triage in this setting
  • Our intervention had three main components: a training package including ETAT, the use of the mHealth tool and study specific protocols. We trained a total of 74 health care workers in collaboration with paediatric registrars at Queens hospital and mHealth collaborators from D-Tree International.We conducted this pilot over a six month period between December 2012 and May 2013 in the 5 busiest primary health clinics in Blantyre District. We focused on paediatrics defining the age range as 0 to 14 following ETAT definitions used within tertiary settings. We monitored patients through the primary to tertiary system. To evaluate the intervention we carried out pre and post intervention activities using a mixed methods approach. I will expand on the intervention and evaluation components in the next few slides.
  • Our triage phone intervention is based on the concept of Chipatala Robots. Robots in the local language are traffic lights and chipatala means health centre. We created Chip as a character to guide patients and carers through the system. Each patient is assigned an ID number through the phones which is also stamped in patient health passports. Health passports are used in Malawi as a record of all clinic visits for each patient. Following assignment of an ID number and entry of basic demographic information of age and sex the health care worker moves through a series of screens asking questions about key clinical signs to identify severe illness. Specific responses take the health worker forward to an assignment based on the traffic lights system where red depicts an Emergency meaning the child is extremely sick and should be seen immediately, green depicts a priority where the child is very sick and should be given priority in the queue or green depicts a queue where the child has a minor injury or illness and should wait in the queue. Patients are then provided with a coloured card according to the outcome of the triage which guides them through the system
  • So this system is about improving pathways within the primary health setting. Let me briefly walk you through the system. A patient enters the primary health centre into the waiting area where health care workers conduct rapid triage using the smart phone including entering demographic details. The patient is then assigned an E, P or Q by the phone and given a coloured card which defines when they will see the clinician. The clinician conducts the consultation and enters further details on a second phone including their own assessment of triage priority and referral decisions. The patient then follows the clinician instructions, passing through the pharmacy as necessary. If they have been referred to Queens hospital their arrival is captured on a third phone kept by AcMen fieldworkers in the A&E department.
  • There have been a range of pilot studies conducted into mHealth applications but these have rarely been accompanied by any rigorous evaluation and a recent systematic review published in PlosMedicine on the effectiveness of mHealth technologies to improve health service delivery processes identified the need for rigorous evaluation and high quality trials measuring clinical outcomes. Our pilot study begins with this rigorous evaluation as a precursor to the development of a cluster randomised trial with clinical outcomes as the main endpoint.
  • Specifically we used both qualitative and quantitative evaluation methods to assess both feasibility and acceptabilityAs I’ve described the mHealth tool was used for the triage algorithm but also to monitor patients through the system using a system of unique ID numbers automatically assigned on initial data entry. This allowed us to assess times taken for different prioritisation groups across all patients.We also wanted to understand whether the training component of the intervention and consistent use of the triage algorithm on the phone had an impact on appropriate decisions regarding severity of illness. Using a self-completed questionnaire and presentation of different clinical scenarios we explored accuracy of E, P Q assignments pre and post intervention.We wanted to explore pathways from a more qualitative perspective to provide more contextual detail for the data produced through the smartphone as a monitoring tool. Patient journey modelling is an approach which facilitates the collation of staff perspectives on the patient journey through the clinic. These were then verified by structured observations of individual patients as they moved through the system.Finally we explored acceptability and response to the intervention amongst different stakeholders including heads of clinics, clinical and triage staff and patients.
  • I will now present some of the results of the evaluation.
  • 41,358 children were triaged through the 5 primary health clinics during the six month study period. Numbers of patients passing through particular clinics did not necessarily reflect size of catchment populations. For example Zingwangwa triaged less people due to under staffing and a high volume of patients and Bangwe had the third highest catchment population but triaged the highest number of patients.
  • Overall we triaged a total of 131 Emergency cases, 13,585 priority cases and 26,452 queue or non-priority cases. Time taken to be seen by the clinician over the pilot intervention period was on average shorter for emergencies, than priorities and longest for queue cases, a finding that was significant and which is as we would hope to see.
  • Of the 41,358 children who went through the triage system the majority were aged 1-5 years across all assessment outcomes. A total of 20,751 children (over 50% of all cases) seen were aged between 1 to 5 years. Of the emergency cases, 37% were <1 and 49% were aged 1-5, whilst only 14% were aged 5y orolder.Of the priority cases, 23% were <1,52% were aged 1-5, and 25% were 5y or older.Of the queue kids, 27% were <1 and 49% were 1-<5, and 24% were 5y or olderSo there was a greater likelihood of an emergency case being aged under 1 year whilst those aged 1-5 were more likely to be given a priority assignment and those over 10 years were most likely to be assigned a queue, showing, as expected, that severity of disease decreases with age.
  • Priority assessments conducted by primary level clinicians were defined as the comparison with which to assess the quality of triage outcomes amongst lower cadres of staff conducting triage. We found in general levels of agreement were high overall between triage and clinician assessments with an above chance level of agreement (Kappa value) of 0.71. 93% of Queue assignments were consistent across triage and clinician assessment, but this was reduced for priority and emergency assessments with75% agreement for priority and only 54.5% agreement for emergency assessmentsThe total figures of 131 are low for Emergency assessments, but we would expect some Emergencies to be recognised immediately, bypassing the triage system. A total of 27 patient carers who reached tertiary care reported that they had not passed through triage due to immediate recognition of severity.
  • The majority of triage within each of the 5 clinics was conducted by Health Surveillance Assistants. These are salaried community health workers employed through the Department of Environmental Health, whose main role is to provide prevention and some basic curative health services at community level. However they are increasingly involved in primary level clinics, taking up a range of roles such as HIV testing and ART provision. There were a total of 10,507 HSAs working across Malawi in 2009, each possessing an average clinical training of 8 weeks from the Ministry of Health.Whilst we originally trained nurses as well as HSAs within each clinic in practice our intervention became the responsibility of HSAs due to low levels of engagement and maternal health priorities amongst nursing staff.
  • We captured data at the A&E department of the tertiary referral hospital around the clock so were able to capture all patients entering the hospital from all 5 participating clinics. Of all patients seen by the primary level clinicians who had been triaged using the smart phone algorithm, 644 or 1.6% were referred to Queens referral Hospital. 9.6% of these had been triaged originally as a queue whilst only 100 of the 131 emergency triage assessments were referred.However, of all those referred to Queens, only 37.3% arrived. This is a large dropout rate within the referral system, although not unexpected and is something we are investigating further.
  • But more positively those who reached Queens did so within a mean time of 5.5 hours. The trend for mean time across all categories reflects that within the primary system where emergencies generally took less time than queues to reach Queens. However, this trend is not significant and this is likely because the sample sizes were small for both Qs and Es.
  • As part of the evaluation we also conducted pre and post pilot intervention patient journey modelling, working with health staff from each clinic to define their perspectives on the primary health clinic pathway. The example here is taken from the clinic with the largest numbers of patients triaged through the phones. We acknowledge that this data is drawn from collective perceptions of health staff but what is interesting to note is that overall impressions of staff were that times taken for each stage of the primary health journey were reported to be noticeably shorter post intervention than pre intervention with the biggest differences reported in the waiting bay and consultation room.
  • The intervention was thus positively viewed by primary health centre staff on the whole. And this perception was reflected in patient carer perspectives on the Chipatala Robots system. Clinic staff also reported that the system had effected a positive change in separating under 5 monitoring visits from those in need of clinical assistance,
  • The intervention was also felt to have improved recognition of severe illness amongst both health workers and patient carers. Most of the health facilities reported that very few deaths occurred on the queues while patients were waiting to be seen by clinicians.
  • So to summarise theAcMen pilot intervention has proven to successfully separate the sick from the non-sick on entry to primary health clinics and we have been able to address some of the problems with primary health pathways that lead to patient dissatisfaction and lack of utilisation of primary health services.We were also able to realign primary level definitions of paediatrics to those of tertiary level definitions, ensuring older children receive appropriate services.We have also proven that triage can be consistently applied over time using the mHealth based algorithm which prevents the user from missing key steps in the process.We worked closely with each clinic in developing the new system to ensure individual clinic settings were taken into account in the design and this translated to high levels of ownership of the intervention, particularly amongst lower cadres of staff. Finally the intervention was found to be highly acceptable to health workers, service users and MoH stakeholders. This was evident in the engagement of the District Health Office in Blantyre providing equipment to carry out triage, involvement in training and clinic system design and in the support we have received for this intervention from the MoH in general.
  • So, returning to the story of Edith where we began. When we went back to see her last year her father said that his one wish was for phcs to improve their recognition of symptoms earlier....This study has shown that mHealth technologies have the potential to improve primary level services with high patient numbers and over burdened staff, one step in the right direction to helping improve early recognition and response to severe illness in children in Malawi.
  • This work has been and continues to be a collaborative effort with different individuals contributing their skillsIt is the outcome of longer-term collaborations between MLW and MRFmHealth technology support has been provided throughout by D-Tree International And it wouldn’t have been possible without the enthusiasm of the MoH through the ETAT office at the MoH and locally through the District Health Office
  • Transcript

    • 1. A pilot study implementing an mHealth triage intervention for health care workers at primary health clinics in Blantyre, Malawi Nicola Desmond Liverpool School of Tropical Medicine Malawi-Liverpool-Wellcome Trust
    • 2. Treatment seeking for acute bacterial meningitis • More than 1 million cases of ABM annually in SSA • Prompt treatment vital to effective management • Late presentation identified as major contributor to high case fatality rates for ABM
    • 3. Responses to ABM HCW diagnosis practices Timeliness dependent on social position Misdiagnosis as malaria Recognition of severity Perceptions of health services Recognition Lay interpretation of symptoms Social validation of illness Financial constraints Action High numbers of patients Unsystematic triage
    • 4. Primary health level contributors High numbers of patients Primary health level misdiagnoses Erratic consultation systems Unsystematic & informal triage
    • 5. Aims  Explore the feasibility of implementing a triage system within PHCs facilitated through the use of mHealth technologies – To develop mHealth algorithm based on Emergency Triage component of ETAT (WHO) – To implement prioritisation system using mHealth triage algorithm – To encourage appropriate referral decisions to QECH & track referrals – To evaluate triage system using mixed methods approaches
    • 6. ETAT for resource-poor settings • ETAT: Emergency Triage, Assessment and Treatment • Component of Integrated Management of Childhood Illness (IMCI) • Identify children with immediately life-threatening conditions • Reliance on few clinical signs • Assessment carried out quickly if negative • Easy to follow guidelines for all cadres with limited clinical background • Easy to conduct when patients queuing
    • 7. mHealth Active prompts Improved diagnosis Consistent Training & monitoring tool
    • 8. The Intervention
    • 9. Pilot study framework Training • ETAT triage • mHealth tool • Study protocols Oct ‘12 Intervention • 5 Blantyre PHC • Bangwe • Chilomoni • Mpemba • Ndirande • Zingwangwa • 0-14 year olds • Monitoring of patient pathways Dec‘12 – May ‘13 Evaluation • Baseline & postintervention • Mixed methods Oct‘12 – June ‘13
    • 10. Chipatala Robots Outcomes EMERGENCY CHILD IS EXTREMELY SICK. TO BE SEEN IMMEDIATELY PRIORITY CHILD IS VERY SICK. PRIORITY MUST BE GIVEN IN THE QUEUE QUEUE CHILD HAS MINOR INJURY/ILLNESS. TO WAIT IN THE QUEUE
    • 11. Improving patient pathways Triage Patient Patient assigned E, P, Q Patient enters PHC HCW conducts rapid triage QECH Fieldworker If referred to QECH data entered on arrival Adapted from Sarah Bar-Zeev (2012) PHC Clinician Patient follows clinician instructions Clinician conducts consultation & enters data
    • 12. The Evaluation
    • 13. Evaluation Quantitative mHealth tool • Monitor patient pathways • Assess if systematic and timely Self completed questionnaires • Explore accuracy of E,P and Q assignments pre and post intervention Qualitative Patient Journey Modelling • Baseline and post intervention • Document practice and patient flows • Structured observations Qualitative Interviews • Capture staff feedback • Impact on overall clinic management and practice
    • 14. Results
    • 15. 41358 Number of Cases Triaged Dec 2012 - May 2013 Total catchment population by clinic Ndirande Zingwangwa Bangwe Chilomoni Mpemba Total 12043 10412 213,613 142,594 131,667 80,940 48,176 616,990 9191 5091 Total Cases Bangwe Ndirande 4621 Chilomoni Zingwangwa Mpemba
    • 16. Mean time between triage and consultation Triage evaluation Time taken (Mins) Paediatric cases E P 28.34 44.64 131 13,585 Q 59.02 26,452 (Anova: P < 0.001)
    • 17. Age distribution of triage assessments 60.0 50.0 40.0 < 1 year 1-5 30.0 6-10 >10 20.0 10.0 0.0 Queue Priority Emergency
    • 18. Triage compared to clinician evaluation 100.0 80.0 60.0 Triage Queue Triage Priority 40.0 Triage Emergency 20.0 0.0 Queue Priority Clinician Assessment Emergency
    • 19. Cadre specific levels of engagement • Health Surveillance Assistants (HSAs) – Salaried community health workers – 10,507 (2009) across Malawi – Average clinical training of 8 weeks • Triage conducted predominantly by Health Surveillance Assistants • Nurses rarely involved in triage of patients
    • 20. Referrals Out of 41,358 children triaged 1.6% (644) were referred to QECH  15.5% (100) - Emergency  74.9% (482) - Priority  9.6% (62) - Queue From the 644 referrals 37.3% (240) arrived at QECH 62.7% (404) of referrals from PHCS did not reach QECH
    • 21. Successful referrals Overall mean time 5.5 hours Triage evaluation E P Time taken (Hours) 3.5 5.7 Paediatric cases 33 193 Q 6.8 14 (Anova: P = 0.39)
    • 22. Patient journey modelling: Bangwe
    • 23. Improved patient flows ‘There is now improvement, those children don’t take long to be attended to.” HCW ‘At Bangwe we are now working together as a team. It is helping us manage the children so much better. We are seeing them far more quickly than before’ HCW ‘In the past even if you come with a child who is very sick your fellow carers could not give you a chance to go in front of a queue for your child to be helped immediately but now things have improved because when a child is very sick s/he is put in front of a queue’ Carer
    • 24. Improved recognition of severe illness ‘Ever since ETAT started, I have never heard any news that a child died on the way or maybe in the doctor’s room’ HCW ‘Triage is being done systematically and children with critical illnesses are being identified and treated on time’ HCW ‘I am so thankful because of what has happened today. My baby was identified among others that he was an emergency and he was taken in front of the queue to be seen immediately by the clinician and he is now better’ Carer
    • 25. Conclusions Health worker wearing Chipatala Robots T-Shirt • Separation of sick from non-sick • Paediatric definitions • Consistent quality of triage • High levels of ownership • High levels of acceptability
    • 26. “I only wish the primary health centres could improve on diagnosis and recognising symptoms quicker...” Mphatso Cheonga, 2012
    • 27. Investigators Naor Bar-Zeev Queen Dube Norman Lufesi Elizabeth Molyneux Sarah Bar-Zeev Rob Heyderman Acknowledgments ETAT trainers Zondiwe Mwanza Thembi Katangwe Yabwile Mulambia Mtisunge Gondwe MRF Thomasena O’Byrne Chris Head Linda Glennie Sara Marshall Rachel Perrin AcMen team at MLW Deborah Nyirenda Bernadetta Payesa Malango Msukwa Alick Masala Lilian Ulayah Farouk Edward Wilard Chilunga Blantyre DHO Dr Owen Malema Dr Eltas Nyirenda Dr Palesa Chisala D-Tree International Dr Marije Geldof Dr Marc Mitchell Phidelis Suwedi All photos reproduced by kind permission of participants Primary Health Centres Bangwe: Martha Makuta Christopher Mkunga Chilomoni: Dalitso Namasani Ndirande: Francis Phiri Mpemba: Rodgers Kuyokwa Zingwangwa: Margaret Chingona

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