The emergence of mobile technology offers new opportunities to improve clinical guideline adherence in resource-limited settings. We conducted a clinical pilot study in rural Bangladesh to evaluate the impact of a smartphone adaptation of the World Health Organization (WHO) diarrheal disease management guidelines, including a modality for age-based weight estimation. Software development was guided by end-user input and evaluated in a resource-limited district and sub-district hospital during the fall 2015 cholera season; both hospitals lacked scales which necessitated weight estimation. The study consisted of a 6 week pre-intervention and 6 week intervention period with a 10-day post-discharge follow-up. Standard of care was maintained throughout the study with the exception that admitting clinicians used the tool during the intervention. Inclusion criteria were patients two months of age and older with uncomplicated diarrheal disease. The primary outcome was adherence to guidelines for prescriptions of intravenous (IV) fluids, antibiotics and zinc. A total of 841 patients were enrolled (325 pre-intervention; 516 intervention). During the intervention, the proportion of prescriptions for IV fluids decreased at the district and sub-district hospitals (both p < 0. 001) with risk ratios (RRs) of 0. 5 and 0. 2, respectively. However, when IV fluids were prescribed, the volume better adhered to recommendations. The proportion of prescriptions for the recommended antibiotic azithromycin increased (p < 0. 001 district; p = 0. 035 sub-district) with RRs of 6. 9 (district) and 1. 6 (sub-district) while prescriptions for other antibiotics decreased; zinc adherence increased. Limitations included an absence of a concurrent control group and no independent dehydration assessment during the pre-intervention. Despite limitations, opportunities were identified to improve clinical care, including better assessment, weight estimation, and fluid/ antibiotic selection. These findings demonstrate that a smartphone-based tool can improve guideline adherence. This study should serve as a catalyst for a randomized controlled trial to expand on the findings and address limitations.
The provision of high-quality clinical care in resource-limited settings is challenged by logistical, educational, and temporal constraints that are exacerbated by a high volume of patients. This is especially true for the management of diarrheal diseases, which may overwhelm health facilities amid outbreaks or seasonal swells of disease. These diseases disproportionately burden poor communities and remain the second leading cause of death for children less than 5 years of age [1–3]. While outpatient decision-support tools like the paper-based World Health Organization (WHO) Integrated Management of Childhood Illness (IMCI) have improved community efforts despite limitations [4–8], inpatient references like the WHO Pocketbook of Hospital Care for Children are often scarce or anecdotally don’t meet providers needs in high-volume situations [9]. These collective challenges manifest in poor guideline adherence and exacerbate the ongoing struggle to improve care for patients with diarrheal disease, especially during large-scale outbreaks. For example, weight-based dosing is fundamental to pediatric care, however equations to estimate weight are rarely both accurate and user-friendly/accessible at the bedside [10–13]. Secondly, the inappropriate use of antibiotics is driving the proliferation of drug resistant pathogens; however prescription behavior change is challenging with complex unpredictable barriers, especially in developing countries [14]. Lastly, complications from basic interventions, such as fluid resuscitation, are more nuanced in resource-limited settings than previously thought [15,16]. Relatively high rates of post-discharge mortality suggest that improved in-hospital efforts might decrease overall morbidity and mortality [17]. This pilot study aimed to address a subset of these challenges by adapting the WHO diarrhea management guidelines onto a smartphone platform and evaluating the approach in resource-limited hospitals. A simple ‘Rehydration Calculator’ was developed based on end-user feedback; functionality included weight-for-age estimation, a dehydration assessment guide, and treatment recommendations. The calculator was evaluated in a clinical study at two remote hospitals in Bangladesh to determine how technology-enabled decision-support impacts standard of care for the management of diarrheal disease.
This pilot study was approved by the Institutional Review Boards (IRB) at the IEDCR (IEDCR/IRB/2015/03) and Stanford University School of Medicine (6208). Written informed consent was obtained from all adult participants and guardians of minors (< 18 years); assent was obtained for children 11–17 years of age. The human experimentation guidelines of the US Department of Health and Human Services were followed during the conduct of this research. Two software modalities were developed and utilized in this study (Fig 1A; S1 Movie; S1 Software). First: the WHO-guidelines were adapted onto a smartphone for decision-support and coined the ‘Rehydration Calculator’ (Fig 1B and 1C). Its development relied on end-user guidance (e. g. field clinical providers) to make it fast, desired, and durable in high-volume, low-resource settings. The calculator captures no personal health information and is not password protected. The calculator does, however, capture basic information to calculate the recommended treatment plan (aka age, gender, watery/bloody stool, five clinical signs of dehydration, allergies, and danger signs). On the back-end, these symptoms are encrypted, stamped with the time and GPS location, and can be aggregated for syndrome-based surveillance. Second: a wireless data collection and aggregation platform (aka the Outbreak Responder platform) was built for the research team (aka ‘response team’). It was developed based on end-user guidance (e. g. field research staff and epidemiologists) to be durable in settings with limited connectivity, yet capture essential epidemiologic data. The user interface is organized as a medical chart and collectively captures critical demographic, syndromic, laboratory, and outcomes information. The sections are ‘Patient Information’, ‘History of Present Illness, ’ ‘Exam’, and ‘Results’ (Fig 1D). The Outbreak Responder captures personal health information and is therefore, password protected, encrypted, and built to industry standards for health information data security. Both technologies are Android-based, function on/off line, and are available upon request (S1 Software). Although the software was built and tested using a locally produced low-cost smartphone (Walton, Bangladesh; 80 USD), a Samsung Note 3 device (200–400 USD) was used in this study because of the improved battery life, ideal screen size (5 in.), and screen responsiveness. A subset of stool samples (first and last patient per day) underwent targeted culture and sensitivity testing for Vibrio cholerae, Salmonella spp. and Shigella spp. [33] in the IEDCR and the International Centre for Diarrhoeal Diseases, Bangladesh (icddr, b) laboratories; stool swabs were placed in Cary Blair media, stored at four degrees Celsius, and transported from the remote field site on a bimonthly to monthly basis. Assays for additional pathogens (e. g. ETEC, cryptosporidium, norovirus, rotavirus) were not performed because the intent of the cultures was to identify antibiotic sensitivity patterns for pathogens that clinically warrant antibiotic treatment, not for comprehensive surveillance. Basic secondary outcomes were collected from the discharge record (e. g. discharge type, mortality). At 10-days post-discharge, the research staff called the patient/guardian to collect secondary outcomes (e. g. readmission, mortality). Data were aggregated and reported by the research staff via the Outbreak Responder platform; the post-discharge calls were also placed from within the software. Patient characteristics were described by percentages for dichotomous (yes/no) variables and by the median, 1st and 3rd quartiles (Q1, Q3) for continuous data. For dichotomous variables, significant differences were assessed by the two-sided Fisher’s Exact test at the 0. 05 level; the risk ratio (RR) was calculated by dividing the proportion of individuals with the event during the intervention period by the proportion of individuals with the event during the pre-intervention period; thus, an RR < 1 indicates the event is less likely to occur in the intervention and an RR > 1 indicates the event is more likely to occur in the intervention. For continuous variables, significance at the 0. 05 level was assessed by either the two-sided Wilcoxon signed rank test (comparison to a median) or the two-sided Wilcoxon rank sum test. When calculating the weight-adjusted IV fluid volume and the recommended IV fluid volume, measured weights were used where available, and otherwise (i. e. , sub-district intervention), estimated weights were used. To calculate congruence during the intervention between IV fluid volume ordered and the recommended IV fluid volume, we used the Wilcoxon rank sum test to test if volume ordered was more than 30% different than what was recommended. Standard of care at both study hospitals relies on a best guess weight estimation without a measured weight, however weight was independently measured to evaluate the weight estimation performed by the calculator. To assess how well estimated weights matched measured weights for children younger than 15 years of age, we calculated the difference between estimated and measured weight and used the Wilcoxon signed rank test to evaluate the differences; measured weights were not adjusted for dehydration status. For groups of patients aged 15 and older, we compared the distribution of measured weights to the gender-specific weight estimates with the Wilcoxon signed rank test. Measured weights during the intervention at the sub-district were unfortunately not obtained, and therefore, to assess how well the estimated weights of patients younger than 15 years of age matched the measured weights, we included district measurements and only sub-district pre-intervention measurements. For those aged 15 and older, we compared estimated weights to district intervention measurements only (since we used measured weights during pre-intervention to obtain estimated weights for these ages). After determining that the weight estimates for children less than 5 years of age were too high, we evaluated WHO percentiles and identified the percentile that best approximated weight for age in this cohort as the percentile that minimized the root mean square error (RMSE) between measured weights and gender-specific WHO percentile estimated weights. All summary statistics and statistical analyses were completed in the statistical software package R version 3. 2. 2 [34]. All data except for a few restricted items are provided in the supplementary materials (S3 Dataset).
A total of 841 patients were enrolled and their records analyzed (Table 1). In the pre-intervention period, we enrolled 325 patients (204 district and 121 sub-district), and in the intervention period, we enrolled 516 patients (430 district and 86 sub-district). Onboarding periods prior to both the pre-intervention and intervention periods were permitted to accommodate for unanticipated additional training and mitigate possible Hawthorne effects (see timeline section). During these periods, 113 additional patients were enrolled but were excluded from the analysis. No patients were excluded for co-morbidities because in practice, these patients were admitted directly to the non-diarrheal wards for more advanced care prior to study screening. Seven patients (0. 8%) had less than 3 loose stools in 24 hours and were excluded from the analysis (see inclusion criteria). Comparing the pre-intervention and intervention periods in the district hospital, we observed significant differences (p < 0. 001) in the proportions of children less than 5 years and adults 20 years and older (Table 1); therefore, we controlled for age in subsequent analyses. A total of 50 doctors participated in the study with participation generally spanning study periods; 35 doctors participated at the district hospital (27 pre-intervention period; 17 intervention) and 15 doctors participated at the sub-district hospital (13 pre-intervention; at least 3 intervention) A total of 277 targeted cultures were performed and identified V. cholerae (N = 19; 7%); Aeromonas spp. (N = 19; 7%), Shigella spp (N = 5; 2%), and Salmonella spp (N = 3; 1%); note that the approach was targeted and did not test for viral pathogens and other common bacterial pathogens. V. cholerae strains were sensitive to azithromycin (19/19) and ciprofloxacin (19/19), had intermediate sensitivity to cotrimoxazole (16/19) and tetracycline (14/17), and were resistant to erythromycin (19/19). One cholera patient was co-infected with at least Aeromonas spp.
The objective of this clinical pilot study was to evaluate the impact of a smartphone adaptation of the World Health Organization (WHO) diarrheal disease management guidelines. The calculator was associated with significant and positive prescription change towards guideline adherence. The findings support our hypothesis that technology-enabled decision-support tools can promote evidenced-based practice in resource-limited settings. With respect to basic medical care, all medical facilities should have a functional and suitable weight scale. However, this minimal standard is frequently untenable in resource-limited settings like those in this study, and therefore, clinicians are forced to estimate weight. We designed the calculator to estimate pediatric weight for age by -1 z-score during the study; however, post-study analysis showed measured weights approximated best to the 5th (females) and 3rd (males) WHO percentiles for children less than 5 years of age while estimates for older age groups did not differ significantly from measured weights. These low percentiles in children under 5 years are not likely to be due to the degree of dehydration at admission alone, but suggest that admitted patients in this age range may be a particularly vulnerable subset of the population. Future larger studies are required to expand on these findings. Additional anthropomorphic measurements (e. g. mid-upper-arm circumference length; MUAC) may also provide evidence that decision-support tools must be built to leverage locally available data in order to optimize weight and other anthropomorphic estimations. In the preparatory phase, we experienced surprising resistance to the use of MUAC bands for weight estimation or for malnutrition assessment [35,36] because of past experience with the time required to take an accurate MUAC and difficulty maintaining MUAC strip supplies. Rehydration Calculator deployment was associated with a reduction in overall IV fluid prescriptions but an increase in the volume ordered when IV fluids were prescribed. Previous studies have shown that transitioning low and medium risk patients towards oral rehydration solution reduces the risk of complications, provides cost-savings benefit to both patients and institutions, and remains effective [37]. The weight measurements enabled the calculator to provide weight-based dosing of fluids for the pediatric patients. The adult patients were often dosed with categorical fluid volumes (e. g. 1,2, and 3 liters) which may be more practical for staff. In the intervention period, the fluid volumes ordered versus recommended for severely dehydrated patients during the intervention were congruent. In a setting like Netrokona District that treats 5000 diarrheal patients monthly, a safe decrease in IV fluid use represents significant overall cost-savings to the medical system while empowering clinicians with the ability to increase fluids volumes to the recommended dose when IV fluids are indicated. Therefore in a venue like Netrokona with low diarrhea-associated mortality, a safe reduction in IV fluid orders may be an important advantage of decision-support tools like the Rehydration Calculator. Future studies in areas with high morbidity and mortality are required to evaluate the physiologic benefits. The Rehydration Calculator was associated with significant and positive antibiotic class-switching to the recommended agent azithromycin. Almost all patients were described as having acute watery diarrhea with moderate or severe dehydration. For these reasons, azithromycin was recommended for almost all patients out of concern for cholera. However, 7% of samples cultured were positive for V. cholerae [all tested strains were azithromycin-sensitive]. The presumed remaining 93% of patients with non-bloody (e. g. non-Shigellosis, non-Salmonellosis, non-Campylobacter) and non-cholera diarrhea likely did not require antibiotics. Although the calculator provided objective dehydration classification, we must also encourage clinicians to accurately report ‘acute watery diarrhea’ and deploy point-of-care diagnostic surveillance for V. cholerae to decrease the prescription of antibiotics. Although there was a small increase (1 day) in the duration of post-discharge days of diarrhea at the sub-district hospital, a larger study will be required to support this finding and determine if this finding has clinical significance. More importantly, this study should spark discussion on the utility of using the phrase “acute watery diarrhea” given that casual use may have contributed to an overuse of antibiotics. A reasonable alternative, despite some culture and physiologic nuance, would be ‘rice-water stool’ because this phrase implies that the clinician highly suspects cholera. The first goal of this study was to improve adherence to WHO-derived guidelines that are evidence-based and considered the gold standard for safety and effectiveness. Although we were only reformatting these guidelines to a smartphone medium, we monitored secondary safety related outcomes, and found only minor differences in length of stay and type of discharge. At the district hospital during the intervention, there was one post-discharge death that occurred after the majority of the diarrheal symptoms had resolved (see hospital and post-discharge course). This event generated a case fatality rate (CFR) of 0. 1% for the entire study (1 out of 954 total patients including patients in the onboarding periods) and 0. 3% for the district intervention which are similar to expected CFRs at similar sites [17,19]. These preliminary data suggest that the Rehydration Calculator is a safe tool to improve guideline adherence for diarrheal disease decision-support. Future studies that include more detailed physiologic assessments may provide further avenues to assess safety and support these initial findings. A second goal was to develop the Rehydration Calculator to be scalable and desired by medical professionals in hospitals like those in Netrokona. Frameworks are being built to guide development and evaluation of smartphone decision-support tools [38]. Scalability of the Rehydration Calculator will likely depend on the use of personal smartphones. This is feasible because the Rehydration Calculator has minimal to no associated cost, captures no personal health information, and does not rely on connectivity. Venues for Rehydration Calculator deployment are broad. For example, Bangladesh and Pakistan both have considerable diarrheal disease, including cholera, and rank among the top ten globally for cell phone penetration per capita [39]. Professionals in these countries are rapidly transitioning to affordable Android smartphones. It also remains possible for institutions to embrace the Rehydration Calculator. This would require purchasing dedicated phones, training, and technical support to maintain the phones. Future studies will require rigorous qualitative and quantitative analysis to further these goals of scalability for both individual and/or institutional deployment. Scalability also relies on desirability. Although an anonymous user acceptance survey was not performed, feedback was obtained in a group format to assess end-user experience. Clinicians expressed that the calculator expedited both clinical assessment and reduced the time required to generate a treatment plan (3–5 minutes). Additionally, use of the calculator conferred a level of professional prestige that was attractive. Physicians were allowed to deviate from the guidelines as needed. Clinicians vocalized that deviation was important when patients expected IV fluids or when emesis made oral rehydration inappropriate. These experiences likely manifested in IV fluid orders for patients with some or no dehydration (Fig 4A). Despite the lack of user acceptance surveys, the design of the Rehydration Calculator appeared to match clinician needs. Future studies are required to explore these preliminary observations. The findings in this study should be viewed within the context of the limitations of the study design and available data. The study site is likely generalizable to remote rural settings with a significant burden of water-borne disease. However, the remoteness also made exhaustive oversight difficult and contributed to the limitations: (i) The first limitation was a suspected under-enrollment of patients at the district hospital during the pre-intervention period compared to intervention period. This suspected under-enrollment may have contributed to the age difference between the pre-intervention and intervention periods; we therefore accounted for age in the analysis. (ii) The second limitation occurred during the intervention period at the sub-district hospital where weight measurements were not obtained due to a work-flow issue. Thus, when evaluating the accuracy of weight estimates, we excluded sub-district patients enrolled during the intervention period. (iii) There was no independent assessment of dehydration during the pre-intervention phase, medications and fluids prescribed were not confirmed to have been administered, and objective physiologic outcome measures were not reliably obtained. The data collection method also relied heavily on the physician transcribing the assessment and plan to the paper chart, and then the researcher transcribing this information to the digital data collection platform. Future studies need a tractable method to validate accuracy through this chain of events; independent assessment would provide added quality control for these steps. (iv) The study was designed as a pilot study, and although the results from the two sites were similar, there was no concurrent control group; this puts the study at risk of temporal variation during the 12-week study. (v) Exclusion criteria were unbalanced because severely malnourished patients were excluded during the intervention because of limited clinical capacity, however, this limitation actually had no impact on the results because no patients were excluded for severe malnutrition alone. (vi) The study was not designed to perform sub-analysis at the level of individual clinicians because of logistical limitations and sensitivity concerns amongst stakeholders. Despite these limitations, the core results from this study were robust and can be viewed as a critical step towards improving the quality of care in remote regions inflicted with diarrheal disease. These findings warrant further investigation with a cluster randomized controlled trial (cRCT). One approach to address physician gestalt and make the cRCT more generalizable and standardized would be to randomize the control study arm to use a paper-based version of WHO guidelines via a laminated card and the intervention arm to the WHO guidelines on the Rehydration Calculator. In conclusion, this pilot study highlights that significant improvement towards guideline adherence can be achieved when clinicians are provided a tool such as the Rehydration Calculator. The study also demonstrates how a simple tool can standardize the assessment of dehydration status, which is a critical first step to understanding physiologic determinants of severe disease and identifying opportunities to improve clinical acumen. Upon further investigation in a cRCT, we hope that tools like the Rehydration Calculator in the hands of frontline providers will contribute to the ongoing drop in morbidity and mortality from diarrheal disease as well as usher in a new era of clinical approach and scientific understanding.
Diarrheal disease is responsible for one in ten deaths among children less than five years of age globally. Innovative interventions to address gaps in the clinical care of these patients are lacking, yet will likely reduce the morbidity and mortality from diarrheal diseases. Therefore, the objective of this pilot study was to take a technology-enabled approach to improve guideline adherence, including antibiotic selection for diarrheal disease management in a resource-limited setting. To do this we adapted WHO guidelines to a smartphone platform and evaluated the approach in Bangladesh at two rural hospitals. The platform was durable and demonstrated positive improvement in guideline adherence. The results suggest that the decision-support tool was associated with a decrease in intravenous fluid use while maintaining safety, an increase in use of the recommended antibiotic, and a decrease in use of medications not recommended. This study represents a critical first step towards technology-enabled decision-support tools for diarrheal disease in resource-limited settings.
dehydration (medicine)
antimicrobials
medicine and health sciences
physicians
medical doctors
pathology and laboratory medicine
engineering and technology
drugs
tropical diseases
microbiology
geographical locations
cell phones
health care
diarrhea
bacterial diseases
research design
health care providers
signs and symptoms
antibiotics
gastroenterology and hepatology
neglected tropical diseases
pharmacology
bangladesh
research and analysis methods
infectious diseases
cholera
pilot studies
communication equipment
people and places
professions
diagnostic medicine
asia
equipment
microbial control
biology and life sciences
population groupings
Evaluation of a Smartphone Decision-Support Tool for Diarrheal Disease Management in a Resource-Limited Setting