scholarly journals Implementation of a referral and expert advice call Center for Maternal and Newborn Care in the resource constrained health system context of the Greater Accra region of Ghana

2021 ◽  
Vol 21 (1) ◽  
Author(s):  
Ebenezer Oduro-Mensah ◽  
Irene Akua Agyepong ◽  
Edith Frimpong ◽  
Marjolein Zweekhorst ◽  
Linda Amarkai Vanotoo
Keyword(s):  
Decision Making ◽  
Call Center ◽  
Clinical Decision Making ◽  
Newborn Care ◽  
Clinical Decision ◽  
Series Data ◽  
Maternal And Newborn Care ◽  
Greater Accra Region ◽  
Maternal And Newborn ◽  
Decision Making Support

Abstract Background Referral and clinical decision-making support are important for reducing delays in reaching and receiving appropriate and quality care. This paper presents analysis of the use of a pilot referral and decision making support call center for mothers and newborns in the Greater Accra region of Ghana, and challenges encountered in implementing such an intervention. Methods We analyzed longitudinal time series data from routine records of the call center over the first 33 months of its operation in Excel. Results During the first seventeen months of operation, the Information Communication Technology (ICT) platform was provided by the private telecommunication network MTN. The focus of the referral system was on maternal and newborn care. In this first phase, a total of 372 calls were handled by the center. 93% of the calls were requests for referral assistance (87% obstetric and 6% neonatal). The most frequent clinical reasons for maternal referral were prolonged labor (25%), hypertensive diseases in pregnancy (17%) and post-partum hemorrhage (7%). Birth asphyxia (58%) was the most common reason for neonatal referral. Inadequate bed space in referral facilities resulted in only 81% of referrals securing beds. The national ambulance service was able to handle only 61% of the requests for assistance with transportation because of its resource challenges. Resources could only be mobilized for the recurrent cost of running the center for 12 h (8.00 pm – 8.00 am) daily. During the second phase of the intervention we switched the use of the ICT platform to a free government platform operated by the National Security. In the next sixteen-month period when the focus was expanded to include all clinical cases, 390 calls were received with 51% being for medical emergency referrals and 30% for obstetrics and gynaecology emergencies. Request for bed space was honoured in 69% of cases. Conclusions The call center is a potentially useful and viable M-Health intervention to support referral and clinical decision making in the LMIC context of this study. However, health systems challenges such inadequacy of human resources, unavailability of referral beds, poor health infrastructure, lack of recurrent financing and emergency transportation need to be addressed for optimal functioning.

scholarly journals The effect of an mhealth clinical decision-making support system on neonatal mortality in Ghana

2019 ◽  
Vol 29 (Supplement_4) ◽  
Author(s):  
H B Amoakoh ◽  
K Klipstein-Grobusch ◽  
I A Agyepong ◽  
P Zuithoff ◽  
M Amoakoh-Coleman ◽  
...  
Keyword(s):  
Decision Making ◽  
Neonatal Mortality ◽  
Neonatal Death ◽  
Clinical Decision Making ◽  
Clinical Decision ◽  
Mortality Data ◽  
Eastern Region ◽  
Intervention Period ◽  
Neonatal Deaths ◽  
Decision Making Support

Abstract Background Mhealth interventions promise to bridge gaps in clinical care but documentation of their effectiveness is limited. We evaluated the utilization and effect of an mhealth clinical decision-making support intervention that aimed to improve neonatal mortality in Ghana by providing access to emergency neonatal protocols for frontline health workers. Methods In the Eastern Region of Ghana, sixteen districts were randomized into two study arms (8 intervention and 8 control clusters) in a cluster-randomized controlled trial. Institutional neonatal mortality data were extracted from the District Health Information System-2 during an 18-month intervention period. We performed an intention-to-treat analysis and estimated the effect of the intervention on institutional neonatal mortality (primary outcome measure) using grouped binomial logistic regression with a random intercept per cluster. This trial is registered at ClinicalTrials.gov (NCT02468310). Results There were 65,831 institutional deliveries and 348 institutional neonatal deaths during the study period. Overall, 47·3% of deliveries and 56·9% of neonatal deaths occurred in the intervention arm. During the intervention period, neonatal deaths increased from 4·5 to 6·4 deaths and, from 3·9 to 4·3 deaths per 1,000 deliveries in the intervention arm and control arm respectively. The odds of neonatal death was non-significantly higher in the intervention arm compared to the control arm (odds ratio 2·10; 95% CI (0·77;5·77); p = 0·15). The correlation between the number of protocol requests and the number of deliveries per intervention cluster was 0·71 (p = 0·05). Conclusions Non-significant higher risk of neonatal death observed in intervention clusters may be due to problems with birth and death registration, unmeasured and unadjusted confounding, and unintended use of the intervention. The findings underpin the need for careful and rigorous evaluation of mhealth intervention implementation and effects. Key messages Supposedly effective interventions must be evaluated in context before they are scaled-up. Mechanisms influencing outcomes in context must be considered in the design and evaluation of interventions.

scholarly journals A hidden Markov model analysis of subject-specific seizure time-series data as a potential aid to clinical decision making

F1000Research ◽  
2016 ◽  
Vol 5 ◽  
pp. 2592
Author(s):  
Martin D. King ◽  
Suresh Pujar ◽  
Rod C. Scott
Keyword(s):  
Decision Making ◽  
Time Series ◽  
Sojourn Time ◽  
Clinical Decision Making ◽  
Time Series Data ◽  
Seizure Activity ◽  
Hidden Markov ◽  
Clinical Decision ◽  
Series Data ◽  
Subject Specific

Background The seizure-count time series data acquired from three children with refractory epilepsy were used in a statistical modelling analysis designed to provide an explanation for the marked variation in seizure frequency that often occurs over time (over-dispersed Poisson behaviour). This was motivated by an expectation that a better understanding of the spontaneous shifts in seizure-activity that are observed in some cases should reduce the risk of over-treatment caused by inappropriate changes in medication. Methods The analyses were performed using Poisson hidden Markov models (HMMs), both Bayesian and non-Bayesian, implemented using Markov chain Monte Carlo and the expectation-maximisation algorithm, respectively. A defining feature of the models, as applied to epilepsy, is the assumed existence of two or more pathological states, with state-specific Poisson rates, and random transitions between the states. Posterior predictive simulation was used to assess the validity of the Bayesian HMMs. Results The results are presented in the form of state transition probability and Poisson rate estimates (i.e., the primary HMM parameters), together with information derived from these primary parameters. State-specific mean-duration (sojourn time) estimates and sojourn-time complementary cumulative probability distributions are the main focus. HMM analyses are presented for three children that differed markedly in their seizure behaviour. The first is characterised by an extreme seizure count on one occasion; the second underwent a spontaneous decrease in seizure activity during the observation period; the third seizure-count time trajectory is characterised by a gradual change in mean seizure activity. We show that, despite their considerable differences, each of the observed seizure-count trajectories can be treated adequately using an HMM. Conclusions The study demonstrates that clinically relevant information can be obtained using HM modelling in three cases with markedly different seizure behaviour. The resulting subject-specific statistics provide useful clinical insights which should aid those engaged in clinical decision making.

Quantitative ultrasound texture analysis for clinical decision making support

2015 ◽  
Author(s):  
Jie Ying Wu ◽  
Michael Beland ◽  
Joseph Konrad ◽  
Adam Tuomi ◽  
David Glidden ◽  
...  
Keyword(s):  
Decision Making ◽  
Texture Analysis ◽  
Quantitative Ultrasound ◽  
Clinical Decision Making ◽  
Clinical Decision ◽  
Decision Making Support

Debates in Dysphagia Management: How Do You Use Evidence-Based Practice in Your Dysphagia Patient Care?

2011 ◽  
Vol 20 (4) ◽  
pp. 121-123
Author(s):  
Jeri A. Logemann
Keyword(s):  
Decision Making ◽  
Patient Care ◽  
Clinical Outcomes ◽  
Clinical Judgment ◽  
Clinical Decision Making ◽  
Evidence Based Practice ◽  
Clinical Decision ◽  
Evidence Based ◽  
Clinical Method ◽  
Do So

Evidence-based practice requires astute clinicians to blend our best clinical judgment with the best available external evidence and the patient's own values and expectations. Sometimes, we value one more than another during clinical decision-making, though it is never wise to do so, and sometimes other factors that we are unaware of produce unanticipated clinical outcomes. Sometimes, we feel very strongly about one clinical method or another, and hopefully that belief is founded in evidence. Some beliefs, however, are not founded in evidence. The sound use of evidence is the best way to navigate the debates within our field of practice.

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