“Quality of prenatal and maternal care: bridging the know-do gap” (QUALMAT study): an electronic clinical decision support system for rural Sub-Saharan Africa

“Quality of prenatal and maternal care: bridging the know-do gap” (QUALMAT study): an electronic clinical decision support system for rural Sub-Saharan Africa

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  CORRESPONDENCE Open Access “ Quality of prenatal and maternal care: bridgingthe know-do gap ”  (QUALMAT study): anelectronic clinical decision support system forrural Sub-Saharan Africa Antje Blank  1* , Helen Prytherch 2 , Jens Kaltschmidt 1 , Andreas Krings 1 , Felix Sukums 1,3 , Nathan Mensah 1,4 ,Alphonse Zakane 5,6 , Svetla Loukanova 2 , Lars L Gustafsson 6 , Rainer Sauerborn 2 and Walter E Haefeli 1 Abstract Background:  Despite strong efforts to improve maternal care, its quality remains deficient in many countries of Sub-Saharan Africa as persistently high maternal mortality rates testify. The QUALMAT study seeks to improve theperformance and motivation of rural health workers and ultimately quality of primary maternal health care servicesin three African countries Burkina Faso, Ghana, and Tanzania. One major intervention is the introduction of acomputerized Clinical Decision Support System (CDSS) for rural primary health care centers to be used by healthcare workers of different educational levels. Methods:  A stand-alone, java-based software, able to run on any standard hardware, was developed based on assessmentof the health care situation in the involved countries. The software scope was defined and the final software wasprogrammed under consideration of test experiences. Knowledge for the decision support derived from the World HealthOrganization (WHO) guideline  “ Pregnancy, Childbirth, Postpartum and Newborn Care; A Guide for Essential Practice ” . Results:  The QUALMAT CDSS provides computerized guidance and clinical decision support for antenatal care, and careduring delivery and up to 24 hours post delivery. The decision support is based on WHO guidelines and designed usingthree principles: (1) Guidance through routine actions in maternal and perinatal care, (2) integration of clinical data todetect situations of concern by algorithms, and (3) electronic tracking of peri- and postnatal activities. In addition, the toolfacilitates patient management and is a source of training material. The implementation of the software, which isembedded in a set of interventions comprising the QUALMAT study, is subject to various research projects assessing andquantifying the impact of the CDSS on quality of care, the motivation of health care staff (users) and its health economicaspects. The software will also be assessed for its usability and acceptance, as well as for its influence on workflows in therural setting of primary health care in the three countries involved. Conclusion:  The development and implementation of a CDSS in rural primary health care centres presents challenges,which may be overcome with careful planning and involvement of future users at an early stage. A tailored software withstable functionality should offer perspectives to improve maternal care in resource-poor settings. Trial registration:  www.clinicaltrials.gov/NCT01409824. Keywords:  Guideline adherence, Clinical decision support systems, Medical informatics, Maternal health services,Pregnancy, Prenatal care, Perinatal care, Millennium development goal, Motivation by information technology, Ruralmaternal healthcare * Correspondence: antje.blank@med.uni-heidelberg.de 1 Department of Clinical Pharmacology and Pharmacoepidemiology,Medizinische Klinik (Krehl Klinik), University Hospital of Heidelberg, ImNeuenheimer Feld 410, Heidelberg D - 69120, GermanyFull list of author information is available at the end of the article © 2013 Blank et al.; licensee BioMed Central Ltd. This is an Open Access article distributed under the terms of the CreativeCommons Attribution License (http://creativecommons.org/licenses/by/2.0), which permits unrestricted use, distribution, andreproduction in any medium, provided the srcinal work is properly cited. Blank   et al. BMC Medical Informatics and Decision Making  2013,  13 :44http://www.biomedcentral.com/1472-6947/13/44  Introduction In 2010, a tragic 287,000 maternal deaths are estimatedto have taken place worldwide. The countries in sub-Saharan Africa (SSA) carried the largest burden of thesematernal deaths (56%), corresponding to an average ma-ternal mortality ratio (MMR) of 500 deaths per 100,000live births and a life time risk of 1 in 39 for a woman todie of maternal causes [1]. In 2010 the MMR was esti-mated to be 460 per 100,000 in Tanzania, 300 in BurkinaFaso, and 350 in Ghana [1]. The range of these figureshas been confirmed by others [2]. Despite an overalldecline of MMR between 1990-2010 of 41% for SSA,the achievement of the Millennium DevelopmentGoal No. 5, which requests a 75% reduction of theMMR between 1990 and 2015, remains a tremendouschallenge [1]. In addition 2.9 million babies died in2011 in the first four weeks of life, most of them indeveloping countries. In SSA this translates into 33deaths per 1000 newborns. While the rate for under-five deaths is declining the neonatal death rate is in-creasing, suggesting that measures around delivery may be of particular importance to influence this ris-ing neonatal mortality rate [3].The availability of skilled and motivated providers iscentral to progress in maternal care [4-6]. Unfortunately  studies have shown that even well trained health staff frequently do not perform to the best of their ability [7-9], and that differences can be observed between how  health providers know a task should be performed andhow they actually perform it. This so-called  “ know-dogap ”  [10] is particularly critical in maternal care whereproviders need to be proactive, observe warning signsclosely, and take appropriate actions rapidly and in an-ticipation of events. Delayed or incorrect decisions may cause either the loss of the mother ’ s or the baby  ’ s life, ormay cause permanent disability resulting in long- termconsequences for an entire family.A major cause of the know-do gap is a health worker ’ slevel of motivation [11]. Motivation at work holds thekey to performance of individuals and organizationsalike [12]. It would seem fair to assume that highly moti- vated workers with suboptimal competence and skillswill not perform well and, conversely, that low motiv-ation may limit the performance of even those healthworkers that command optimal levels of competence. Inaddition, other factors such as the culture of a profes-sional environment or the openness for learning, also in-fluence performance.In an ideal world, knowledge is correctly applied by motivated health workers in an environment of appreci-ation and professional exchange. Interventions to im-prove clinical performance promise better chances of success if they improve health worker competence andmotivation, as well as the work environment.Today, health care information technology (HIT) pro- vides tools that facilitate the application of knowledge atthe point of care and thereby simplify adherence toguidelines, which can improve practitioner ’ s perform-ance, and ultimately patient care. These promisingeffects have been confirmed in developed countries[13,14]. Meanwhile, resource-poor countries have startedto explore and apply the opportunities of HIT systems[15] and consider this as one of the key areas of develop-ment activities presently. Amongst other benefits, HITsystems are seen to hold the potential for such countriesto leap-frog technological steps of development [16].However, improvements of this nature can only mate-rialize if HIT systems are customized to the needs of localpractitioners and take the challenging environment of resource-poor countries into consideration. Attentionneeds to be paid to the specifics of the user population,environmental demands, and implementation challengesin rural areas with unreliable access to electricity, techni-cians, and more highly trained cadres of health workers.In addition, systems have to be thoroughly tested prior totheir widespread application as experiences in the devel-oped world have shown that HIT systems may also haveunforeseen detrimental effects on patient care [14,15]. If HIT systems are thoroughly developed and pilotedthey hold the potential to support the competences of local health workers by providing easy access to learningtools, or even information exchange to local and distantcolleagues, and possibilities for continuous updates of treatment guidelines. Computerized decision supportmay even catalyze decision-making in situations wherethere is time pressure and no possibility to seek advicefrom other professional colleagues. Indeed, such supportmay reassure health staff working in professionalisolation and serve to improve the work environment,enhance motivation, and increase the experience of competency. The very use of Information Technology (IT) can be fun and motivating in itself [17] and using awell designed HIT, whilst experiencing increased compe-tency, should improve clinical performance. “ Quality of prenatal and maternal care: Bridging theknow-do gap (QUALMAT) ”  is a research project fundedas part of the 7th Framework Programme of theEuropean Union (grant agreement 22982). It is a collab-oration between the Centre de Recherche en Santé deNouna (Burkina Faso), Ghent University (Belgium),Heidelberg University (Germany), Karolinska Institutet(Sweden), Muhimbili University of Health and AlliedSciences (Tanzania), and Navrongo Health ResearchCentre (Ghana). The study seeks to improve the quality of such care at primary facility level in Ghana, BurkinaFaso, and Tanzania. The study works on the assumptionthat health provider ’ s competence and motivation inter-act with the work environment to give rise to the work Blank   et al. BMC Medical Informatics and Decision Making  2013,  13 :44 Page 2 of 16http://www.biomedcentral.com/1472-6947/13/44  effort that produces clinical performance [18,19]. One of the main aims of this study was to achieve the develop-ment of a clinical decision support system (CDSS) forpregnancy care in resource-poor environments and toanalyse its potential to positively impact upon thecompetence and motivation of maternal care providersas well as their work environment, thus improving thequality of care.The objective of this paper is to describe theQUALMAT study and the process of the development of the QUALMAT CDSS prior to its use. A second inter- vention within the project is the introduction of perfor-mance-based incentives, the details of which will bereported elsewhere. Observation of the use of the CDSSin the given environment will later allow conclusions tobe drawn regarding the usefulness of such tools forhealth care provision in rural, resource-poor settings. Methods The QUALMAT study settings The CDSS is part of a complex intervention plan withinthe QUALMAT study. The development of the CDSSpreceded the interventions at the study sites. Study ac-tivities of the project are carried out at rural primary health care centres in the three countries in SSA(Figure 1). In each country both intervention and con-trol districts were chosen according to several criteria.The study districts were particularly disadvantaged basedon comparisons within the countries. Study sites arepublic facilities at least 10 km away from a town andwith a minimum of infrastructure. Availability of electri-city was not a prerequisite and was organized throughthe project where necessary. All the health centresinvolved in the study have maternity facilities equippedto accommodate uncomplicated deliveries including a24-hour observation period after delivery. However,none of the sites is equipped or staffed to provide fullemergency obstetric care, for example with assisted vagi-nal delivery methods. Staff members at the sites arehealth professionals with 1-3 years of training, but nophysicians are present. All health centres included in thestudy are no more than 2 hours drive from a districthospital where patients can be referred. Ambulancesfrom the district health authorities are available upon re-quest (Ghana and Burkina Faso, partly Tanzania) or am-bulances are part of the facilities (most facilities inTanzania). The local district hospitals provide full emer-gency obstetric care including the possibility for caesar-ean sections. One district with 6 study sites in eachcountry will be subject to the intervention package,whereas the control district will have no interventions.In Ghana the intervention district is Kassena  –  Nankanaand the control sites are located in Builsa. In BurkinaFaso the intervention district is Nouna and the controlsites are in Solenzo. In Tanzania, the intervention dis-trict is Lindi Rural and the control sites are situated inMtwara Rural.The QUALMAT project consortium, in close collabor-ation with local health authorities at national and sub-national level, has planned to introduce a CDSS and a GhanaBurkina FasoTanzania Figure 1  Countries and specific study districts participating in the QUALMAT project. Blank   et al. BMC Medical Informatics and Decision Making  2013,  13 :44 Page 3 of 16http://www.biomedcentral.com/1472-6947/13/44  performance-based incentive scheme in the researchsites of the intervention districts. The development andintroduction of these interventions is accompanied by research projects, whereby the quality of maternal care,the competence and motivation of those providing suchcare, and the cost of the interventions is assessed before,during, and after their introduction. In addition, specificstudies will be conducted to evaluate the CDSS toanalyse adoption and usability of the CDSS, attitudestowards computer use, and the influence of the decisionsupport on medical care and on the workflow at thehealthcare centresEthical clearance for the QUALMAT study was pro- vided by the Ethics Committee of the Medical Faculty,University of Heidelberg, Germany (ref. S-173/2008), theInstitutional Review Board of the Navrongo HealthResearch Centre, Ghana (ref. NHRCIRB 085), theMuhimbili University of Health and Allied SciencesEthical Review Committee, Tanzania (ref. MU/RP/AEC/Vol.XIII/96) and the Ethics Committee for Health Re-search, Burkina Faso (ref. 2010.05/CLE/CRSN). QUALMAT CDSS development The development of the QUALMAT CDSS tried to an-ticipate critical factors for success of clinical decisionsupport as identified by Kawamoto and colleagues [14].Future users, medical experts, and IT specialists jointly developed the CDSS using the following steps: 1. Assessment of needs, definition of software scope,and assessment of software interfaces2. Programming of pilot software with iterations afterdiscussion with future user3. Programming of the final software and 3 iterativetest phases aimed to ensure high quality of the finalsoftware (2 software release candidates with several versions)4. Final implementation During initial visits to study sites, the local situation of the rural health care facilities in all three countries andthe workflow of patient care at the sites were assessed.Future users - mostly, as expected, computer illiteratesor individuals with minimal computer experience - andlocal representatives of the health system from both na-tional and sub-national levels described their environ-ment and outlined their expectations.There had been no previous HIT projects in thetargeted districts for interventions with the exception of Ghana, where the expansion of the national health insur-ance scheme included the introduction of computers foradministrative work at the faculties. In general, HIT pro- jects previously known to the project partners in SSAwere mainly focused on facilitating administrative work.Therefore the concept of a CDSS initially required de-tailed discussions to reach a common understanding re-garding the conceptual possibilities of decision supportat the point of care. In a second step, about 15 months June 2009May 2014    E  v  a   l  u  a   t   i  o  n  :  a  s  s  e  s  s  m  e  n   t  o   f … performance based incentives    I  n   t  e  r  v  e  n   t   i  o  n April 2012 project management  b  a s  el  i  n e an al   y  s i   s i  n t   er v  en t  i   on an al   y  s i   s  O b  s  er v  a t  i   on of  i  n t   er v  en t  i   on s  … policies/ guidelines preparation and implementation of … acceptance/ usability/ influence on workflow of CDSS CDSS development and implementation of … provider motivation… health economic cost… quality of maternal care Figure 2  The QUALMAT study: QUALMAT interventions, including the CDSS and accompanying studies. Blank   et al. BMC Medical Informatics and Decision Making  2013,  13 :44 Page 4 of 16http://www.biomedcentral.com/1472-6947/13/44  into the project, a pilot software showing possible func-tionalities and options was programmed and representa-tives from all levels of the health system, includingfuture users as stakeholders of the project were askedfor their feedback in personal meetings. This was doneafter future users and individuals from district health au-thorities had had the opportunity to experiment withusing the pilot software. In addition, local research part-ners investigated the prototype in detail. Feedback onfunctionality, style, and administrative necessities of thesystem were received and directed the developmentphase of the software. At that stage the future usersand district health authorities were by now familiarwith the concept of decision support, and were very active in expressing their expectations and in pro-posing changes and additional functionality. Thedevelopment team was well briefed for the finalprogramming and a first release candidate of the final version was rolled out after an additional 6 months.This release candidate underwent testing and wastranslated into local languages before being forwardedto the study sites 3 months later.The test phase for the release candidates included afirst part, where the system was tested with use casesand mock patients and a second iteration, where real pa-tient data were entered retrospectively, without usingthe system for patient care. Care was taken, that a thor-ough test phase with all pre-planned steps was com-pleted even when it became obvious that timelines forthe actual start of the implementation were delayed. Arepeated test phase with the second release candidatewas added for a final feedback on all changes prior toimplementing the final version. The detection of possibleproblems with the software before its use for patientcare was given highest priority. Translation of the systemproved to be challenging, whilst conducting the neededtraining sessions and feedback rounds took a great dealof effort on the part of the local research team. This toowas considered essential to ensure that the softwarecould be successfully introduced in the routine care at Figure 3  Decision support by electronic checklists: Guidance through routine actions in maternal and perinatal care is provided bychecklists to ensure thorough clinical and laboratory work-up during antenatal care visits. Blank   et al. BMC Medical Informatics and Decision Making  2013,  13 :44 Page 5 of 16http://www.biomedcentral.com/1472-6947/13/44
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