systems strengthening for quality mnh service data management: experience from 4 pilot hospitals

7/29/2019 Systems strengthening for quality MNH service data management: Experience from 4 pilot hospitals

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Systems strengthening for quality

MNH service data management:

Experience from 4 pilot hospitals

Global Maternal Health Conference

Arusha, Tanzania; 16, Jan 2013

Dr. Nancy A. Kidula

Snr. RH/FP Advisor

Jhpiego Kenya


2/19

The problem

While Kenya does have a strong HMIS, indicatorscollected on MNH at the facility level are limited

data is often incomplete, inaccurate,

reporting is poor- Some indicators not captured data is rarely available for use in planning

lack of data limits MNH service availability andquality

Improvement of MNH data management andutilisation is the top priority of the Kenya MNH

Road Map (August 2010)


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Testing a solution

USAID MCHIP is partnering with DRH/MOPHS

to pilot an MNH surveillance system in Kenya

GOK/DRH

M-CHIP MNHSurveillance


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Step 1: Advocating for change

1. USAID - MCHIP Kenya

sought concurrence from

DRH/MOPHS for the pilot

2. Site Selection

Used based on predefined

criteria

Four public district Hospitals

selected

3. Baseline Survey done toassess facility readiness and

situation analysis

Map with sites circled


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Results of Baseline Survey

Different versions ofMNH data tools in use

Data recording /capturing wasinconsistent,incomplete

MOH designated codeswere rarely used

Use of the partographin labour was poor

Health serviceproviders in all facilities

had not received anyupdates or in-service

training in the BEmONC

National MNHguidelines not available

at point of use

BEmONC equipmentsets were incompleteor missing; supplieswere lacking or not

available at point of use

Data was rarely utilisedat facility level for for

decision making


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Common situation- before intervention


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Step 2: Capacity Building

Workshop

3 days

5 people from eachfacility

Administrator,Nursing officer incharge, Maternity in-charge, Medicalrecords, midwife

Introducedimportance of datamanagement

How to generate data

Practicum: Sharedtheir data sets andchallenges

Site Visits

Participantsidentified site specificproblems

Participants

identified areas thatdid not requireproject help

Procurement of basicsupplies;

reorganisation ofservices;

maternal andperinatal deathaudits,

communication etc;

OnsiteInterventions

Training:

Data managementand use

Service skill updatesin key MNHinterventions

e.g., Pre-eclampsia,neo-natalresuscitation, etc.

Tools

Job aids, guidelines,policies

Data collectiontools (sup. Register,data use wkbk,

Supportivesupervision and

mentorship

Weekly visits from theMCHIP/MOH team

Worked with all clinicalstaff

Observation and on-jobcoaching

Feedback sessions anddiscussion

Training on MNH skills

Monthly DataSupervision visits

Worked with records

personnel and nurses Reviewed reporting

tools

Audited summary tools


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Results:

Outputs: At least 60 health workers

have been updated in

EmONC skills and in datamanagement

Procured basic equipment

and supplied

Reorganized work flow

Outcomes: More complete and

accurate reporting of MNH

indicators including thepartograph use

Data is being used at the

facilities for decision

making- MDR; timelyreferrals, partograph


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Well and fully completed partograph


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Box withemergency

supplies for

management of

ecclampsia

ready for use in

the labour ward


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Maternity Registers appropriately & well filled


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Data Reporting and presentation enhanced;

facilitates interpretation and use


13/19


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Impact

? Reduced

Maternal and

Perinatal

Morbidity and

Mortality


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Quotes from the field

We were practicing

witchcraft before the

HBB training.

(Dr at Makindu Hospital)

The updates have really helped us . someof us are retiring and had never been

updated. You are removing us from mud.(Matron at Gilgil Hospital)

As nurses we feared

magnesium sulphate but

now we can use it and

save lives.(Nurse-in-charge Makindu

maternity)


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Key Insights

Numbers alone are not useful they have to

be goodnumbers

What makes a GOOD number?

Common definition of what the indicator is

attempting to capture

Everybody is counting the same thing in the

same wayE.g. Asphyxia and resuscitation


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Conclusion

For an effective MNH surveillance system, data

management skills building MUST BE

accompanied by:1. Clinical skills standardization &

2. Systems strengthening


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Acknowledgements

DRH/MOPHS- Kenya

Administration and Staff of

Naivasha, Makindu, Gilgil andKangundo Hospitals

USAID MCHIP- Washington-Barbara and Maya

USAID MCHIP Kenya team

Jhpiego Kenya


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THANK YOU!

systems strengthening for quality mnh service data management: experience from 4 pilot hospitals

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