Loxodonta africana) is listed as vulnerable, with wild populations threatened by habitat loss and poaching. Clinical pathology is used to detect and monitor disease and injury, however existing reference interval (RI) studies for this species have been performed with outdated analytical methods, small sample sizes or using only managed animals. The aim of this study was to generate hematology and clinical chemistry RIs, using samples from the free-ranging elephant population in the Kruger National Park, South Africa. Hematology RIs were derived from EDTA whole blood samples automatically analyzed (n = 23); manual PCV measured from 48 samples; and differential cell count results (n = 51) were included. Clinical chemistry RIs were generated from the results of automated analyzers on stored serum samples (n = 50). Reference intervals were generated according to American Society for Veterinary Clinical Pathology guidelines with a strict exclusion of outliers. Hematology RIs were: PCV 34–49%, RBC 2.80–3.96 × 1012/L, HGB 116–163 g/L, MCV 112–134 fL, MCH 35.5–45.2 pg, MCHC 314–364 g/L, PLT 182–386 × 109/L, WBC 7.5–15.2 × 109/L, segmented heterophils 1.5–4.0 × 109/L, band heterophils 0.0–0.2 × 109/L, total monocytes 3.6–7.6 × 109/L , lymphocytes 1.1–5.5 × 109/L, eosinophils 0.0–0.9 × 109/L, basophils 0.0–0.1 × 109/L. Clinical chemistry RIs were: albumin 41–55 g/L, ALP 30–122 U/L, AST 9–34 U/L, calcium 2.56–3.02 mmol/L, CK 85–322 U/L, GGT 7–16 U/L, globulin 30–59 g/L, magnesium 1.15–1.70 mmol/L, phosphorus 1.28–2.31 mmol/L, total protein 77–109 g/L, urea 1.2–4.6 mmol/L. Reference intervals were narrower than those reported in other studies. These RI will be helpful in the future management of injured or diseased elephants in national parks and zoological settings.The African elephant ( Loxodonta africana) is a megaherbivore, which had an extensive range across the African continent until the 1930s. Loss of habitat and poaching led to the present classification of this species as Vulnerable by the International Union for Conservation of Nature (IUCN) (Loxodonta africana africana (South African bush elephant) based on the geographical distribution (The African elephant (e (IUCN) . Accordie (IUCN) . The subribution . Elephanribution . Additioribution .Elephas maximus), mainly in Sri Lanka, Thailand and Myanmar, presumably because of the easier access to this species, as these animals are at least partially under human care ]  are one of the most valuable diagnostic tools in veterinary medicine and are used to help differentiate diseased from healthy individuals , 8. Theyman care –17. Some (ISIS)] .The objective of this study was to establish RI for hematology and selected clinical chemistry measurands for a free-ranging African elephant population. The RI were generated in accordance with the guidelines published by the American Society for Veterinary Clinical Pathology (ASVCP)  with minEthical approval specifically for this study was obtained from the University of Pretoria Faculty of Veterinary Science Research Ethics Committee and Animal Ethics Committees (certificate number REC 132-19).via a 18G needle and direct vacutainer collection from an auricular vein at first handling after the venipuncture site was swabbed with alcohol (ethanol 70%). Disposable medical gloves were worn during the blood collection. Whole blood was collected in sealed EDTA and serum vacutainers . Serum samples were left to clot for at least 30 min standing upright in a cooler box. Samples were transported cooled, until they were processed in the VWS laboratory within 6 h of collection. EDTA whole blood was analyzed with the scil Vet abc  or the Horiba ABX Micro VS60  hematology analyser. Automated hematology analysis was not routinely performed for all animals. Blood smears were made using a standard pushing (wedge) technique . Serum tubes were centrifuged at 1,300 g for 10 min, and serum aliquoted into cryotubes  and frozen at −80°C.The samples for this study originate from the free-ranging African elephant population from the KNP, South Africa. The animals were immobilized for park management purposes or other unrelated studies. Immobilization was performed by Veterinary Wildlife Service (VWS) veterinarians according to SANParks Standard Operating Procedures (SOP). Elephants were darted from a helicopter using an air-pressurized dart  propelled by a carbon dioxide powered rifle . Immobilization was induced with etorphine , azaperone , and hyaluronidase , with dose ranges based on subjective weight and age estimation by the same veterinarian (etorphine 0.003 mg/kg and azaperone 0.01 mg/kg). Once the elephant was recumbent, the ground crew approached and assisted the elephant into lateral recumbency, if required. At the end of the procedure, naltrexone  was administered intravenously at 20 times the etorphine dose (mg), and the animal observed until it had fully recovered. Sample collection proceeded according to a standardized protocol as follows: blood was taken echnique  and staiAt the time of immobilization, a physical examination was performed. Animals without injuries and free of clinical abnormalities were considered healthy. All data for the animal, including the sex, general condition, age and weight estimation, microchip number and geographical location of the immobilization site, were recorded in Excel spreadsheets. Notes were added for abnormal clinical findings or injuries, if present. Sample selection was made according to this information. All selected samples were collected between October 2014 and August 2019, meaning they were stored no longer than 5 years. This threshold was chosen as no studies could be found on stability beyond this time . SamplesData from the original hematology analyses were reviewed. Analysis was performed using EDTA whole blood after mixing at room temperature, with a Scil Vet ABC (first 25 results) and a Horiba ABX Micros ESV60 (last 11 results), using the domestic horse setting on both analyzers. These analyzers and settings have not been validated for elephant blood. Internal quality control using manufacturer-supplied quality control material was performed every day before analysis. These results from the original hematology analyses were reviewed for the present study. Firstly, the automated calculated hematocrit (HCT) was compared to a manual PCV performed at the same time. Only automated results with a HCT within 3% of the PCV were included in this study. The white blood cell count (WBC), red blood cell count (RBC) and platelet count (PLT) as measured by impedance , and the hemoglobin concentration (HGB), as measured by a cyanide-free photometric method  were considered accurate enough to be used for this study. The erythrocyte indices mean cell volume (MCV), mean cell hemoglobin (MCH) and mean cell hemoglobin concentration (MCHC) were calculated using the following standard equations:12/L, PCV %; MCV fL, MCH pg, MCHC g/L) as well as the severity of toxicity (1+ to 4+) according to a standardized grading system described for domestic species . MorpholAnalysis was performed using the Abaxis VetScan VS2  according to the manufacturer's instructions. The Large Animal rotor  was used and included the following measurands: albumin, alkaline phosphatase (ALP), aspartate aminotransferase (AST), calcium, creatine kinase (CK), gamma glutamyltransferase (GGT), globulin, magnesium, phosphorus, total protein (TP) and urea. The analytical methods for these measurands are shown in Statistical analyses were performed with MedCalc software version 19.1.7  and the Excel add-on Reference Value  Advisor version 2.1  accordinp-value of >0.2 for the Shapiro-Wilk test, not >0.05, was used to define a Gaussian distribution  or robust method . The 90% confidence intervals (CI) of the lower and upper reference limits (RL) were calculated using a bootstrap method. The ratio of the upper or lower CI to the RI was calculated by dividing the former by the latter .To examine the potential effect of storage time on clinical chemistry measurands, the number of days from storage to analysis was enumerated, and the correlation between days in storage and measurand concentration or activity calculated using Pearson's correlation coefficient, Samples were selected from free-ranging elephants, with no overt clinical abnormalities, living in the KNP in South Africa and were collected between October 2014 and August 2019. Of the original 79 selected samples, two were excluded after further detailed analysis of their capture records, as one was found deceased subsequent to a road traffic accident, and one had a snare injury. After statistical analysis and removal of 28 complete data sets with outliers, 51 samples from apparently healthy animals remained. These consisted of 42 males and 9 females. All were sub adults or adults according to the original selection datasheet, except for one male calf which was estimated to be 4 years old. Results for PCV were available for 48 of these animals. Only 23 samples from the automated hematology analysis were finally included, after applying the described screening procedures. Results thereof are shown in The CVs obtained for the clinical chemistry measurands from the repeatability study with elephant serum on the VS2 were: albumin 1.1%, ALP 3.1%, AST 4.7%, calcium 0.8%, CK 5.2%, GGT 6.6%, globulin 3.7%, magnesium 0.9%, phosphorous 1.0%, TP 1.4%, urea 3.9%. Imprecision for all measurands was considered acceptable, when compared to total allowable analytical error guidelines for veterinary species .r = 0.69, p < 0.001; CK r = 0.50, p < 0.001). In other words, older samples appeared to have a decreased AST and CK activity compared to more recent samples, which may indicate a storage effect (No significant correlation between storage time (minimum 5 months to maximum 5 years) and measurand concentration/activity was found for any measurands apart from AST and CK , published between 1977 and 1980; all these data originated from the same elephant population in eastern Africa (n = 18–23), where on later examination, infestation with the bile duct hookworm  and other parasites was discovered  and a different subspecies of African elephant (Loxodonta africana cyclotis)   report dStudies on Asian elephants are more common and involve both managed and free-ranging animals. Managed elephants often reside in tourist camps or are working elephants from the timber industry, from various locations in South East Asia, including India, Sri Lanka, Thailand and Myanmar , 17, 36.In the above mentioned studies on Asian elephants, the animals were described as clinically healthy or apparently healthy. Sample sizes varied greatly; the smallest one was performed on six individuals with longitudinal samples  with the12) and PCV (49%) described in the East African study, were higher than those determined in the current study and the minimum-maximum (1.4–6.0 × 1012/L and 18–80%) ranges much wider, in comparison to the current reference intervals (12/L) and PCV (35.1%); MCV range (106–122 fL) was slightly narrower and MCHC range (310–390 g/L) wider but comparable to the current RIs , reported in the African forest elephant (n = 5) (9 /L) from captive and immobilized African elephants (9/L) in tuskers (9/L) in free-ranging elephants in Sri Lanka, the latter being the highest reported WBC count in apparently healthy elephants  are lower than the current RIs (0.4 × 109/L)  , 39. Int× 109/L) .9/L) (9/L) (9/L)  . The lowL) (9/L) . The higelephant . The widMost of the results for clinical chemistry measurands reported in other studies of comparable geographical region were similar to the current measurements, even though analytical methods differed , 32, 43.Serum mineral concentrations in the African elephant vary by geographical location. The means of calcium, magnesium and phosphorus in the current study were higher compared to those from elephants at Sengwa Wildlife Research, Zimbabwe, but lower in comparison to elephants from Ruwenzori National Park in Uganda , 30. FirDetermination of AST and CK activity is especially important for captured animals, as increases in these enzymes are associated with muscle injury, intramuscular injection, trauma and capture stress in domestic and wild animals such as dogs, horses, some ruminants and rhinoceros –48. AnimLoxodonta africana are even lower whereas urea and globulin means were higher than the current results  from the same geographical location (KNP), using the same drug combination and clinical chemistry panel, did not consider the influence of the anesthetics relevant  without significant changes in the measurands , 62. In For future studies, the sample size should be increased, especially for the automated hematology analyses, as the current sample size was rather small and the analyzers are not validated for this species. Ideally, the same machine should be used for all samples, which in the best case scenario is validated for the species to be analyzed. Unfortunately, this was not possible for this study due to resource restrictions present in the laboratory. It is important to note that the automated hematology reference intervals are specific to the analyzers and settings used in this study. The desirable sample size for all measurands would be >120, but above 39 is considered reasonable for RI studies, which we were able to attain for blood smear analyses and WBC count as well as chemistry measurands . Larger This is the first RI study for hematology and a clinical chemistry panel relevant for the African elephant performed using appropriate statistical methods and a strict outlier elimination approach. In comparison with previously performed research on the same species and for Asian elephants, the current ranges are narrower, which will have a greater potential to identify normal and abnormal clinical pathology results in individuals of this species in the future. The established RI will function as an important tool for researchers and clinicians working with the species, provided that drugs, geographic location and nutrition are taken into consideration when interpreting results. Future studies will be needed with greater sample sizes to create RIs for different groups .The original contributions presented in the study are included in the article/The animal study was reviewed and approved by Veterinary Science Research Ethics Committee and Animal Ethics Committee (certificate number REC 132-19), Faculty of Veterinary Science, University of Pretoria.CS contributed to conceptualization and study design, data collection, data curation, performed the data analysis, and wrote the first draft of the manuscript. EH contributed to conceptualization and study design, data collection, data curation, performed the data analysis, and acquired funding. JH contributed to data collection. MM contributed to conceptualization and design of the study, and assisted with sample provision. PB assisted with sample provision and data curation. All authors contributed to manuscript revision, and read and approved the submitted version.The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.


Primary Care Networks (PCNs) are a new organisational hierarchy with wide-ranging responsibilities introduced in the National Health Service (NHS) Long Term Plan. The vision is that PCNs should represent ‘natural’ communities of general practices (GP practices) collaborating at scale and covering a geography that fits well with practices, other healthcare providers and local communities. Our study aims to identify natural communities of GP practices based on patient registration patterns using Markov Multiscale Community Detection, an unsupervised network-based clustering technique to create catchments for these communities.Retrospective observational study using Hospital Episode Statistics - patient-level administrative records of attendances to hospital.General practices in the 32 Clinical Commissioning Groups of Greater Londonst April 2017 and 31st March 2018.All adult patients resident in and registered to a GP practice in Greater London that had one or more outpatient encounters at NHS hospitals between 1The allocation of GP practices in Greater London to PCNs based on the registrations of patients resident in each Lower Layer Super Output Area (LSOA) of Greater London. The population size and coverage of each proposed PCN.3 428 322 unique patients attended 1334 GPs in 4835 LSOAs in Greater London. Our model grouped 1291 GPs (96.8%) and 4721 LSOAs (97.6%) into 165 mutually exclusive PCNs. Median PCN list size was 53 490, with a lower quartile of 38 079 patients and an upper quartile of 72 982 patients. A median of 70.1% of patients attended a GP within their allocated PCN, ranging from 44.6% to 91.4%.With PCNs expected to take a role in population health management and with community providers expected to reconfigure around them, it is vital to recognise how PCNs represent their communities. Our method may be used by policymakers to understand the populations and geography shared between networks. In the absence of data-driven approaches, Primary Care Networks (PCNs) have formed through interpersonal relationships between practices rather than through an understanding of the distribution of their registered patients.This study uses Markov Multiscale Community Detection, a data-driven, unsupervised clustering method, to identify ‘naturally occurring’ communities of general practices (GP practices) to collectively form 165 PCNs across London.In doing so, this technique produces PCNs which are most representative of the spatial communities of patients for whom PCNs provide care.National Health Service England have proposed that PCNs should contain 30 000 to 50 000 patients restricted to a single Clinical Commissioning Group, however we find this may not represent patterns of care delivery in an urban setting.The use of Hospital Episode Statistics ensures that the obtained PCNs are related to secondary care utilisation; yet, on the other hand, these PCNs may not reflect patients who rarely use healthcare services but remain registered to a GP practice.The introduction of Primary Care Networks in the National Health Service (NHS) Long Term Plan in 2019 marks one of the biggest changes to general practice in England.3 4The new NHS Long Term Plan and GP contract announced in 2019 introduced the ‘Primary Care Network’ (PCN),5While many practices are already members of networks or federations, or informally collaborate with other GPs, the new organisational structure may not match existing arrangements. According to NHS England, PCNs are specified to be networks of neighbouring practices covering a population of 30 000 to 50 000, which will not cross the boundaries of a CCG, although this is not an absolute requirement.In this article, we set out an approach to defining communities that conform to the criteria required of PCNs, based only on the registration of patients from a given geographical area to a GP. Our approach uses Markov Multiscale Community Detection (MMCD), which uses Louvain optimisation to detect and obtain robust partitions of a network, without imposing a priori the number of partitions that should be produced.st April 2017 to 31st March 2018 were identified from Hospital Episode Statistics (HES).All adult patients presenting to outpatient secondary care in England from 1In cases where a patient was registered at more than one LSOA and GP practice combination within the 1 year time period of our study, the record with the highest frequency for that individual was chosen; where these were tied, the most recent combination was chosen. GP practices contributing fewer than 100 patients were excluded.In order to quantify the extent of overlap between areas covered by different GP practices in London, the equivalent market size (EMS) of each LSOA was calculated as the reciprocal of the Herfindahl-Hirschman Index , with clear surrounding margins of suboptimal values.The geographical co-ordinates of each GP practice were identified from their registered postcode. For each of the 16 000 partitions produced, the pairwise geographical straight-line distances between all practices within each PCN was calculated. Where the median distance from a practice to all other practices within a PCN community was more than four times the median pairwise distance between all practices within the PCN community, this practice was excluded as a spatial outlier. The number of practices within each PCN community was calculated and the proportion of practices contained within the polygon drawn around the outer geographical limits of the practices comprising the PCN community was calculated.PCN communities where the number of practices was less than 3 or greater than 20, or where the percentage of practices spatially located within the outer spatial limits (defined by the polygon drawn around each practice coordinate) of the PCN community was more than 25% were excluded. The total number of practices present in the remaining PCN communities was calculated. The partition with the highest number of included GP practices was taken as the optimal partition.For this optimal partition, the GP practice name, location and CCG were linked by practice code, using data from NHS Digital. Practice list sizes as of March 2018 were also linked using data from NHS Digital.25LSOAs were subsequently assigned to a PCN community based on the PCN to which the highest number of patients within a given LSOA were registered. GP practices were mapped along with their corresponding assigned community allocation and LSOA boundaries. The proportion of patients resident within the same PCN as their registered GP was calculated for each PCN.The data on which this study is based was granted following review by a panel including patient and lay representatives. Patients were not invited to contribute to the writing or editing of this document for readability or accuracy.st April 2017 and 31st March 2018  was seen, from 1.1 to 20.6. 185 LSOAs (3.8%) had an equivalent market size of less than two GP practices, consistent with primary care provision by a single dominant GP practice. The median equivalent market size across London was 4.9 GP practices per LSOA, while 259 LSOAs (5.4%) had an equivalent market size of more than 10 GP practices. The median LSOA-level equivalent market sizes of CCGs in London ranged from 3.2 to 7.0. Overall, the median equivalent market size for LSOAs north of the river Thames was 23% higher than those south of the river (5.3 vs 4.3).Residents in the same LSOA in London were generally registered to a wide range of GP practices. An optimal configuration of GP communities was found at an RMST parameter of 5.5 and Markov time of 0.054. For this optimal clustering, only 43 GP practices  were unassigned to a community according to our criteria: 28 practices lay in 20 communities with only 1 to 2 practices, and a further 15 practices were spatial outliers. Collectively, these 43 practices were the modal provider of primary care for 114 LSOAs with a total population of 187 101 (2.3%). Our optimal partition consisted of 165 PCNs grouping 1291 practices, which cover 4721 LSOAs in London and 97.7% of the estimated London population. A map of this optimal configuration, displaying GP practices superimposed on the LSOAs assigned to each PCN is shown in The PCNs ranged in size from 3 to 18 practices with a median of 8. Median list size of PCNs was 53 490 patients . Around two-thirds (67.9%) of PCNs contained practices from only one CCG and the remaining PCNs contained practices from either two (23.0%) or three (9.1%) CCGs.Across all 4721 LSOAs, the median percentage of patients registered to GP practices located within their allocated PCN was 73.7%, ranging from a minimum of 24.8% to a maximum 98.6% , left. AAs the health system develops to prioritise integrated and collaborative working, we need to improve our understanding of the relationships between providers and patients. With the requirement for GPs to form larger organisational units in the form of PCNs, there is a need to quantify how representative these communities are of their populations. The techniques used in our study provide an unsupervised, data-driven means of producing mutually exclusive PCNs formed by bringing together GP practices that frequently provide care to patients from the same geographical regions. In doing so, it makes no assumptions regarding geography or organisational hierarchies and produces ‘natural’ communities. Using this technique, we showed that despite having no prior geographical knowledge, 97.7% of the population of London may be assigned to a PCN of appropriate size and which are spatially consistent.The factors that determine practices joining a PCN are complex and rely on the interpersonal and professional relationships between GPs as much as the shared relationships in registration of the local population. Furthermore, predefined boundaries cannot be ignored, with local authorities and CCGs taking responsibility for commissioning for a given geographical area and registered patients. However, networks must work for their population, and existing tools to guide decision-making are limited. While most practices have now joined PCNs, it is likely that boundaries will change as practices move in and out of PCNs, reflecting the complex dynamics of a system which needs to work for the population and providers.In the context of the PCNs that are forming across London, our findings may in the future be directly compared with the actual PCNs that form. While it is hoped that the PCNs suggested by this study conform to those being formed across London, identifying discrepancies between the two conformations may offer a means of identifying PCNs which may not adequately represent local patterns of patient registration, and therefore may benefit from reorganisation. Second, where practices struggle to align with one another to form PCNs, the findings of this study offer a tool by which to propose suitable PCN structures.Our findings also raise questions regarding the optimal size and configuration of PCNs across London. Our modelling suggests that the 30 000 to 50 000 recommended list size for PCNs may be too restrictive. The finding of a median list size for our optimal configuration of 53 490 in London, with an IQR from 38 000 to 73 000 patients suggests that the current recommended range may not permit larger networks to form where underlying patient registration patterns would favour this. A wider range of list sizes also suggests that greater variability in PCN size should be allowed depending on local need. Similarly, while a clear majority of the PCNs we found (67.9%) are formed from practices within a single CCG, almost one-third contain practices in two or three CCGs, suggesting that there should be flexibility in conformity to CCG boundaries. However, there may be additional problems for a PCN containing practices from more than one CCG where priorities do not align.If PCNs take on a role in population health management, and community services are required to reconfigure to match the footprints of PCNs,Population dispersion is likely to be a greater problem in a densely populated urban area such as London, where there are extensive transport links and a greater choice of providers within a shorter commuting distance. However, our estimates, which by design are modelled to represent optimal patterns of registration, are likely to overestimate coverage of the PCN compared with those PCNs that have already formed. The opportunity for patients to retain registration with a practice after moving outside of its historic catchment postcodes may reduce coverage of a PCN where a fixed relationship to a discrete geographical area is required.The emergence of digital NHS general practice, in the form of GP at Hand and its peers which allow patients access to video consultations as the first point of contact, disrupts the notion of a ‘place-based’ relationship between primary care providers and geographical communities of patients. Currently, the registered population for such services is disproportionately of working age, with 98.5% of registrants between 20 to 64 years of age, compared with a London-wide estimate of 75.3%.As such innovations in registration and GP provision mature and potentially scale across the health system, the significant differences between patients using digital primary care services and traditional general practices may undermine efforts to enforce relationships between primary care providers and discrete geographical communities of patients. This trend may signal the emergence of a differentiated system of primary care, where those with low care needs are served by essentially rootless digital primary care providers and those with higher care needs are attended to by well-integrated, accessible primary care providers with a nearby physical presence. In such cases, the creation of PCNs by providers of digital primary care may be orthogonal to the underlying ethos of vertical integration and investment in relationships with community care providers that underlies the current policy.One of the main limitations of our analysis is in use of secondary care data, rather than primary care data. While HES represented 3.4 million patients in London, this covers less than half of the estimated population of 8.8 million.A limitation with our method was that the optimal configuration was unable to fit every practice in London to a PCN, with 43 (out of 1334) unassigned as a result of our selection criteria. Fifteen practices were unassigned as spatial outliers, which is to say that their median distance to all other practices in their network was more than four times greater than the median pairwise distance between all practices in the network. These rare instances may reflect the statistical noise of the modelling technique which is agnostic to the spatial proximity of providers to one another. A further 28 practices were unassigned due to their proposed PCNs containing fewer than three practices. In these cases, allocation of unassigned practices in collaboration with practices and commissioners, to nearby larger PCNs could be an appropriate solution to ensure complete allocation of practices to PCNs. The finding that many unassigned practices were near the periphery of London suggests a boundary effect where the exclusion of practices and the population outside of London may have affected the model in these regions.As health systems adapt towards closer integration across services, network analysis offers a data-driven and unbiased means of understanding the connections between PCNs and their patients. Our findings demonstrate that GP practices may be combined into communities reflecting their underlying populations in accordance with the specification of PCNs. At a time when integration of community, primary and secondary care is being prioritised, concurrently, place-based primary care anchored in the local community is increasingly being challenged with the growth of online GP consultation providers, such as that provided by GP at Hand in London. Upscaling primary care into larger networks has the potential to weaken further the ties between providers and their communities. There is a pressing need to better understand how these networks will represent their geographies and patients, to identify who may gain and who may lose out, and ensure a well-intentioned policy does not widen inequalities in health.


This current study provides novel insights into the performance of rice genotypes under varying As stress during different growth stages for further use in ongoing breeding programs for the development of As-excluding rice varieties for As-polluted environments.Rice remains a major staple food source for the rapidly growing world population. However, regular occurrences of carcinogenic arsenic (As) minerals in waterlogged paddy topsoil pose a great threat to rice production and consumers across the globe. Although As contamination in rice has been well recognized over the past two decades, no suitable rice germplasm had been identified to exploit in adaptive breeding programs. Therefore, this current study identified suitable rice germplasm for As tolerance and exclusion based on a variety of traits and investigated the interlinkages of favorable traits during different growth stages. Fifty-three different genotypes were systematically evaluated for As tolerance and accumulation. A germination screening assay was carried out to identify the ability of individual germplasm to germinate under varying As stress. Seedling-stage screening was conducted in hydroponics under varying As stress to identify tolerant and excluder genotypes, and a field experiment was carried out to identify genotypes accumulating less As in grain. Irrespective of the rice genotypes, plant health declined significantly with increasing As in the treatment. However, genotype-dependent variation in germination, tolerance, and As accumulation was observed among the genotypes. Some genotypes  showed high tolerance by excluding As in the shoot system. Arsenic content in grain ranged from 0.12 mg kg Oryza sativa L.) is one of the most vital staple grains of the world, 90% of which is produced and consumed in Asian countries, and it influences the livelihoods of several billion people  and less toxic pentavalent arsenate As(V)  and in rainfed upland paddy soils (aerobic conditions), oxidized arsenate As(V) dominates  is physicochemically analogous to the essential nutrient phosphorus (P), and arsenite As(III) uses the active uptake linked to aquaglyceroporin mediate acquisition pathways to gain access into the plant system  can substitute for inorganic phosphate in a variety of biochemical processes that affect key metabolism in the cell. In contrast, As(III) having high affinity toward sulfhydryl-containing enzymes interferes with key enzymes in a deleterious way  constitutes the major As species loaded into rice plants  species dominate  species dominate  ⇌ As(V). Arsenate As(V) dominates during drying and arsenite As(III) dominates during wetting  program were systematically evaluated under varying As stress aiming at understanding the genetic variation in germplasm for germination capacity, As tolerance, shoot exclusion, and low grain accumulation. We combined three different screening strategies to recognize appropriate genotypes for As tolerance and exclusion during different growth stages. We hypothesized that the selected rice genotypes would show a diverse range of morphological and As accumulation trait variability for As-induced toxicity stress. The main objectives of this study were to (a) identify the germination ability of 53 genotypes facing varying As stress and cluster them into different As stress-tolerance groups, (b) explore the genetic potential of individual genotypes for As tolerance and exclusion at the early growth stage of rice and cluster them into different As stress-tolerance groups by shoot exclusion and inclusion groups, and (c) identify the most significant genotypes that accumulate less As in grain and categorize them into different As grain-accumulating groups. By combining these three different screening strategies, we explored the interactions of As tolerance and exclusion traits during different growth stages.Fifty-three genotypes involving seven IRRI-GSR advanced fixed lines and 46 different rice cultivars from the core breeding collection of the IRRI-GSR breeding program  were sys2, Sigma Aldrich, Singapore) to evaluate the germination ability of seeds. All the purified seeds of the rice genotypes were oven-dried for 5 days at 60°C to break any residual seed dormancy and were surface-sterilized with 1% sodium hypochlorite (NaOCl) for 1 min, followed by rinsing with deionized water several times. Germination was evaluated on a moist filter paper (Whatman No. 1) bed dampened with 10 ml of the respective As treatment placed on each of the 15-cm-diameter Petri dishes. Fifty sterilized healthy seeds per genotype were laid on each Petri dish and incubated at room temperature (28–32°C) for germination. Each treatment was replicated three times and the seeds were allowed to germinate for 10 complete days. During this period, the Petri dishes were moistened with the respective solutions of As when required. After 10 days of incubation, the number of seeds germinated was recorded by the emergence of the radicle and coleoptile for the respective treatments and controls. Based on the germination percentage under different As treatments, the genotypes were placed into four different categories: highly tolerant (>80% germination), moderately tolerant (>50% germination), moderately susceptible (<50% germination), and highly susceptible (<20% germination). The tolerance percentage of the genotypes was calculated with the following equation:The seed germination screening assay was carried out in the plant growth chamber facility at IRRI for evaluating the germination tolerance of genotypes under varying As treatments. Five concentrations of inorganic As treatments, 0 ppm (control), 5 ppm (low), 10 ppm (medium), 15 ppm (high), and 20 ppm (very high), were supplied in the form of sodium arsenate . The control treatment without As in the nutrient solution was maintained throughout the experiment. The plants were grown under As-toxic conditions for 18 days, with the pH adjusted to 5.4 every other day and nutrient solutions were completely renewed every seventh day. The experiment was laid out as a complete randomized design with three independent replicates, and five repeats per line in each replicate, leading to 32 hydroponic trays, each accommodating up to 100 seedlings. To compare the differences among the lines, the relative chlorophyll concentrations were measured non-destructively from the base, middle, and tip of the uppermost leaves of each plant, and the average values were expressed as SPAD units  as the indicator of leaf senescence caused by toxic As treatment. The responses of the plants to the As treatments were evident at 18 days after treatment. Changes in the shoot and root length response to the As treatments were measured for each entry at 18 days after treatment. Shoot length was measured from the base of the plant to the tip of the longest leaf, whereas root length was measured from the base of the plant to the root tip. Three plants per entry per replicate were rinsed with deionized water to remove any excess nutrients sticking to the surface of the plants and then oven-dried for 3 days at 70°C to remove moisture. Before sample processing for As analysis, dry biomass was recorded.Seedling-stage As stress screening experiments were conducted in a hydroponic system in the controlled phytotron glasshouse facility at IRRI. Optimum rice-growing conditions were maintained throughout the experiment: 29/21°C (day/night) temperature, 70% relative humidity, and natural light. Seeds of the 53 genotypes were oven-dried for 5 days at 60°C to break any residual seed dormancy and incubated at 30°C for 48 h for pre-germination. One seedling per genotype was transferred per well with 1 cm diameter on a Styrofoam seedling tray with the size of 28 × 32 × 1.25 cm having 100 wells (10 × 10) with a nylon net bottom fixed in a dark plastic tray containing 8 L of full-strength Yoshida nutrient solution . Twenty-one-day-old seedlings were transplanted to the field with the experiment laid out in a randomized complete block design in two-row plots with 12 plants per row at a spacing of 20 × 20 cm. Standard agronomic practices with optimum fertilizer application, irrigation, and plant protection measures were carried out to ensure a good crop growth cycle for grain development. At maturity, eight middle plants of each genotype were harvested in bulk. The harvested seeds were oven-dried at 50°C for 3 complete days and de-husked brown rice was analyzed for As accumulation.Field screening for As accumulation in grain was conducted at the field station of IRRI during the 2014 dry season. IRRI is situated at latitude 14°13′N and longitude 121°15′E, and the paddy soil type is a Maahas clay loam, isohyperthermic mixed-type tropical soil with average total As content in soil ranging from 2.6 to 7.2 mg kg3), followed by 2 ml of hydrogen peroxide (H2O2), and 1 ml of deionized water was added and pre-digested overnight in the fume hood .The root and shoot samples from the hydroponic screening experiment and grain samples from the field experiment were analyzed for total As content. Dried samples were thoroughly homogenized by an ultra-centrifuge mill  modified with a tungsten blade to avoid any cross-contamination. Ground 0.2–0.5 g of sample was added to a closed-vessel digester, with 5 ml of high-purity 69% concentrated nitric acid  was carried out to observe the pattern of variation among the 53 rice genotypes, the relationship among individuals, and their characteristics. Originally, relative index values were derived by assessing the response to the control value for respectively measured traits  and absolute values for As content in shoot, root, and grain. Then, the index and absolute values from different screening strategies were combined to identify the correlation of the response variable vectors and genotypes. The analyses were performed using JMP®, Version <16>  design with three true treatment replicates (independent containers) and three randomized blocks within each treatment set containing all 53 genotypes. Two-way ANOVA was carried out to observe the effects of lines, treatment, and line-treatment interaction on different As stress-induced traits, and Tukey's honestly significant difference test was used for of means . For grap < 0.01) (< 5 ppm As (60.69%) < 10 ppm As (41.19%) < 15 ppm As (27.30%) < 20 ppm As (16.06%). The 53 genotypes were grouped into four clusters based on the relative tolerance percentage under varying As treatments  . The meaeatments . Genotyps stress . Except −1), and As shoot excluders (<12 mg kg−1)  to 27.85 mg kg−1 (Xing-Ying-Zhan) and root As content ranged from 119.86 mg kg−1 (Huang-Hua-Zhan) to 146.54 mg kg−1 (BR11). The overall results indicated that adequate genetic variability is present among the studied genotypes for As accumulation in young seedlings for As stress. The concentrations of As in the seedlings of the 53 genotypes are presented in Exposure to varying concentrations of As for 18 days induced a significantly negative response among the investigated 53 genotypes. Chlorophyll content, plant height, and root length showed significant variation between the control and 15 ppm As treatment. Root and shoot length and biomass accumulation were negatively influenced by As treatment. Accumulation of As in the shoot and root tissue increased significantly with the increase in As in the treatment . There wmg kg−1) . The 53 mg kg−1) . In the −1 also showed higher variation among the genotypes in grain As accumulation (−1 in Huang-Hua-Zhan (indica subspecies) from China to 0.48 mg kg−1 in IRAT 109  from Brazil. Based on As accumulation in the grain, the genotypes were classified into three groups: low As content rice (<0.2 mg kg−1), moderate As content rice (<0.3 mg kg−1), and high As content rice (>0.3 mg kg−1) . Genotypes from indica subspecies  and japonica subspecies (NPT-IR68552-55-3-2) accumulate less As (<0.2 mg kg−1) in the grain. High-yielding mega-variety IR64 also accumulated less As (<0.2 mg kg−1) in the grain.Unpolished brown rice of the 53 genotypes harvested from the field with soil As content <7 mg kgmulation . Among tmg kg−1) . The ricr correlation coefficient among various measured As stress-responsive traits from germination tolerance in the early growth stage of rice, and grain As content screening revealed the existence of a complex association between the studied traits , relative germination indices  and relative root-shoot morphological parameters  of 53 rice genotypes, and PCA best describes the response to identify As-tolerant and low-accumulating genotypes. Of the total variation (34.8%), the first two principal components (PCs) accounted for 19.8 and 15% among the genotypes, respectively . The fir(III), a neutral molecule is the most dominating As species. It enters plants through aquaporin channels, primarily through the nodulin 26-like intrinsic proteins   is the most prevalent As species in aerobic soils, accounting for just a small proportion of total As in flooded paddy soils. However, in rice, OsHAC1;1 (Loc_Os02g01220) and OsHAC1;2 (Loc_Os04g17660) are responsible for As(V) reduction to As(III) to facilitate As(III) efflux out into the external environment  was used in the treatment in this study  at the germination stage for 10 days. Similar concentrations frequently occur in the topsoil of the rice-growing regions of Bangladesh and West Bengal, India. The presented results revealed the toxic effect of As and germination ability decreased significantly with increasing As concentration in the treatment [0 <5 <10 <15 <20 ppm As(III)] which is consistent with the results of previous studies  treatment with high accumulation in the shoot .In hydroponics As stress, irrespective of the rice genotype, the overall plant performance decreased with an increasing concentration of As in the nutrient solution . Arsenicindica cultivars tend to accumulate higher amounts of inorganic As in grain and shoot than japonica cultivars  and displayed strong susceptibility in germination and seedling-stage experiments as they were unable to exclude As from the shoot system. Similar results were observed with the same genotypes in field-based experiments  in grain and also displayed moderately resistance in hydroponic screening by excluding As in the shoot, but it displayed susceptibility in germination tolerance. In our study, we explored the genotypes commonly used by breeders to develop varieties for Bangladesh and India. In developing rice-growing Asian countries, straw is either burned in the field or used as fodder for cattle, which further increases the risk of As exposure , WTR1, GSR IR1-5-Y4-S1-Y1, OM997, and Zhong413 were tolerant against a high concentration of As and they also accumulate very low concentrations of As in the grain. These identified As-tolerant genotypes would contribute greatly to the development of As-excluding varieties for contaminated ecosystems. Recently, genotype WTR1 was nominated in Bangladesh and released as BRRI dhan69, making a significant impact on mitigating the problem of As contamination.Our study demonstrates screening strategies for recognizing suitable tolerant and As-excluding germplasm, and offers great potential for use in targeted breeding programs for As-contaminated regions. Irrespective of the As concentration in the treatment, germination, overall plant health, and tolerance percentage declined drastically, signifying that the presence of As harms rice production. For future screening of rice germplasm, 10 mg kgThe original contributions presented in the study are included in the article/JA, MF, and VM conceived the research. VM and JA created the rice diversity panel. VM conducted the As screening experiment and collected the data. VM, FZ, AW, AP, L-BW, and JM worked on the data analysis and drafting of the manuscript. VM, JA, MF, JM, and ZL reviewed the manuscript. All authors read and approved the manuscript.https://nbn-resolving.org/urn:nbn:de:hbz:5n-55609).The authors would like to thank and acknowledge the Bill & Melinda Gates Foundation for providing a research grant to ZL for the Green Super Rice project under ID OPP1130530. The work reported in this manuscript was part of VM's Ph.D. thesis funded through the Green Super Rice project at IRRI (The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article, or claim that may be made by its manufacturer, is not guaranteed or endorsed by the publisher.


Electronic Health Records (EHRs) are now widely used to create a single, shared, and reliable source of patient data throughout healthcare organizations. However, health professionals continue to experience mismatches between their working practices and what the EHR allows or directs them to do. Health professionals adopt working practices other than those imposed by the EHR to overcome such mismatches, known as workarounds. Our study aims to inductively develop a typology of enduring EHR workarounds and explore their consequences by answering the question: What types of EHR workarounds persist, and what are the user-perceived consequences?This single case study was conducted within the Internal Medicine department of a Dutch hospital that had implemented an organization-wide, commercial EHR system over two years ago. Data were collected through observations of six EHR users  and 17 semi-structured interviews with physicians, nurses, administrators, and EHR support staff members. Documents were analysed to contextualize these data .Through a qualitative analysis, 11 workarounds were identified, predominantly performed by physicians. These workarounds are categorized into three types either performed while working with the system (in-system workflow sequence workarounds and in-system data entry workarounds) or bypassing the system (out-system workarounds). While these workarounds seem to offer short-term benefits for the performer, they often create threats for the user, the patient, the overall healthcare organization, and the system.This study increases our understanding of the enduring phenomenon of working around Electronic Health Records by presenting a typology of those workarounds that persist after adoption and by reflecting on the user-perceived risks and benefits. The typology helps EHR users and their managers to identify enduring types of workarounds and differentiate between the harmful and less harmful ones. This distinction can inform their decisions to discourage or obviate the need for certain workarounds, while legitimating others.The online version contains supplementary material available at 10.1186/s12911-021-01548-0. Electronic Health Records (EHRs) have been widely implemented because of their promise of improved patient service, quality, healthcare safety, and reduced costs . EHRs ar‘workarounds are behaviors that may differ from organizationally prescribed or intended procedures. They circumvent or temporarily ‘fix’ an evident or perceived workflow hindrance in order to meet a goal or to achieve it more readily’  then I can copy-paste that to the in-basket and have the physician prescribe it. […] Otherwise, I have to figure it all out myself, Google it and so on.” (I_MA3).The expected benefit of this workaround is for the user. First, an improved workflow and perceived time savings are achieved when copy-pasting instead of discretely registering the data. Second, a fuller overview is created. As one medical administrator explained: “convenience and support [the EHR] offers when registering discretely” (I_PH5), and the hindrance it causes when conducting research with the data present in the EHR. As one physician explains: “you can create all kinds of reports if the data are registered discretely; you cannot do that if it is only plain text” (I_PH4).The likely risks of copy-pasting affect the user, the system, and the hospital in terms of extra work due to the loss of system support as the performers of this workaround miss out on the “They are using something called a specialty comment to just to write when they should be discussing the patient.” (I_SS2).The second in-system data entry workaround is using separate text fields in addition to the required data fields in the EHR. This workaround was not seen during observations but mentioned during interviews with three members of the support team, three physicians, and two nurses. A member of the EHR support team commented that she observed this workaround put into practice by physicians during multidisciplinary meetings: “the only option that is available now” (I_SS2). One nurse explained that if she wants to report a patient’s status, she “can only enter a fixed number of words or characters” (I_NU2). The lack of space to make comments creates her need “to add a separate note” (I_NU2). Another reason for this workaround is the lack of an overview of the relevant data for a particular physician or specialism: “[…] a problem list is a problem list, but I also see about fifteen other problems which I might not find interesting at all” (I_PH5). In this situation, the separate note field is used to restructure issues in the problem list. A final reason is the lack of knowledge about the system’s functionality and which is the right button to click. As an alternative, “they just put it in a note” (I_SS2).In some cases, the underlying reason for adding separate note fields is the lack of functionality in the EHR, and it is “good and concise summary of all the relevant information” (I_PH5). The risks associated with this workaround affect the hospital and the user. Registering data as a separate piece of plain text makes it harder to generate data from the EHR for research purposes. A second risk is that the use of separate note fields leads to a potentially incomplete overview if applied extensively: “It is a like a toilet roll from which all the sheets have been torn, you have to read through 100 notes? 200 notes? The slightest inconvenience experienced by a patient will have a note allocated to it” (I_PH3).A possible benefit of this workaround is that, through these separate notes, healthcare providers create a better overview for themselves in the form of a “I use [the EHR] in the way I want to use it, within certain boundaries. I take a certain degree of freedom. […] There is always more than one way to skin a cat, and I know we have to work with [the EHR]. But, inside [the EHR], I believe everyone should be able to take their own paths, within the frameworks we set along the way” (I_PH1).We found the third in-system data entry workaround was not entering certain patient-specific information—deliberately leaving data fields empty. This workaround was observed with one physician and mentioned during interviews with five other physicians. A reason for this workaround is that users feel that the EHR restricts their autonomy and directs them to enter data they do not wish to enter: “Yesterday I registered a patient that was already known to another specialism. This took me two hours. Two hours to order all this data in the EHR. Imagine that. Normally I would do that too, but I just wrote it down, and it cost me 10 min” (I_PH3). As is clear from the quote above, not filling in certain data fields in the EHR is expected to benefit the user by saving the physicians time to spend on other activities. On the other hand, there are associated risks for the system and the patient. One physician highlighted that the EHR “only works well if we all use it correctly” (I_PH5), meaning that this workaround damages the information quality that the EHR can provide. Moreover, not registering data potentially leads to cascading errors in medicine prescriptions: “Certain disciplines order an antibiotic treatment and do not include an end date, so it just stays in the system. The patient probably stopped taking the antibiotics a while ago, but the system says otherwise. This, in turn, has consequences for my prescriptions” (I_PH1).Another related reason is that entering data is time-consuming. A physician mentioned: “I find this very regretful, but I know these things happen” (I_SS4). Further, a physician explained a situation in which this would occur: “This is often over the phone: the physician is busy elsewhere. I can imagine that when he picks up the phone and is asked ‘May this patient receive that medicine’, he might say: Just fix it, I’m busy” (I_PH5).The fourth in-system data entry workaround identified is sharing login details with other employees, which is a clear violation of hospital policies. This workaround was not observed, and most interviewees denied sharing login details with colleagues. However, five members of the support team recognized this took place: “there is no computer to hand” (I_NU2). All the interviewees were doubtful that this workaround has overall benefits. One physician felt “that it saves you a certain amount of work, but that does not stack up against the risks” (I_PH5). While positive effects were hardly mentioned, the interviewees did identify the risks of sharing login details with co-workers. One member of the support team explained the implications for patient safety: “Assume that a person with insufficient knowledge orders or gives the wrong medication. Imagine a patient being allergic to a type of medication but receives it anyway. In the worst case, they die. This can happen if you start acting in someone else’s name. It is very dangerous” (I_SS4).Most interviewees explained that sharing login details would be done either because of a lack of time or a lack of physical facilities—when “you never know if these details will be used again without your knowledge” (I_PH1).A risk for the user is that once login details are shared, there is no control over potential abuse of these details, “I received the exact same pop-up as four weeks ago, to pay extra attention to the patient’s medication. Now I can’t continue, I have to enter new data into the system. Otherwise I cannot prescribe medicines. So what happens is, you are just going to make it up, you know, you just want to get on” (I_PH1). The nurse in mentioning this workaround recognized it in the following situation: “even though I emptied the catheter bag at 11 pm, I enter this as emptied at 9.59 pm” (I_NU1).The final in-system data entry workaround is entering data that does not represent reality. This workaround was not observed, but described by a physician and a nurse. As an illustration, the physician explained: “[The EHR] should not rule over us, it should help us. I mean, we need to use [the EHR], but it should not be the case that it dictates how I should do my job” (I_PH1). In the second, the nurse explained she has no other option than to use this workaround as in the EHR “a day needs to be finalized at 10 pm […] That’s just how it is designed” (I_NU1).In the first instance, this workaround was used deliberately to bypass the restricting power of the system over the work process to avoid delays in ordering medicines. As a physician explained: “other alarm bells starting to ring” (I_NU1). The nurse did not expect this workaround to bring about dangerous situations since “30 min or an hour’s difference is negligible” (I_NU1). The physician said that, as with ignoring pop-ups, entering incorrect data may lead to a false sense of safety, negatively affecting the patient.In the nurse’s case, there is an expected benefit of registering incorrect data for the users: it avoids a distorted image of the previous day improving workflow the next day. All activities scheduled for the previous day need to be registered by 10 pm to avoid In addition to the several workarounds that are used within the EHR system described above, our data also demonstrate that people bypass the EHR system by using other systems or relying on other routines. These out-system workarounds include writing down information on paper, using one or more shadow systems, giving verbal consent for dispensing medication, and detaching a scanner from the COW (EHR- Computer-on-Wheels) to take it into a patient’s room.“When I do my round of patient visits, I always have a piece of paper with me. […] I would rather write some keywords on paper, sit behind my desk, think about it, and register the information in peace.” (I_PH1). We also observed that nurses write patient information on their hands with the intention to enter it into the EHR at a later stage.First, many interviewees recognized the use of paper for making notes. This workaround was observed once and mentioned twelve times during interviews with nurses, physicians, members of the support team and medical administrators. In effect, users rely on paper in combination with the EHR: “The patient might be very nervous, in such cases, you want to make eye contact. An option would be to bring your computer, but then you would be talking to the screen instead of to the patient. […] You look less at the patient’s face, so you don’t see the impact of what you are saying. […] And the patient could feel less heard” (I_NU1).As described above, the physician feels that first writing down information on paper and later registering it in the EHR helps to process the information, suggesting a lack of trust in their own abilities to directly register the data correctly. Another motive for using paper is to maintain eye contact with patients. As one nurse explained: “get lost or lie around” (I_NU3). Further, patient safety is jeopardized as there is a possibility “to overlook items because they are not on the work lists on your computer, especially if you don’t know that patient well” (I_NU2).The expected benefits of paper are for the user and for the patient. First, the user saves time that can be used to process and reconsider the information provided by the patient. Second, patient contact is preserved. Risks for the user are a loss of overview, extra work and a potential loss of data as paper might The second out-system workaround identified is the use of a system other than the EHR. Two physicians, four members of the support team, and one medical administrator admitted to using Microsoft Word and Microsoft Excel either as a substitute for or as a complement to the EHR.“Every day there are three, four, five notes added for each patient. These are all separate, so I cannot just scroll through them. So, open, close, open, close. Then I also have to remember each note’s content. Therefore, I open a Word document next to it, to create my own file” (I_PH3). Another reason for using shadow systems is a lack of functionality in the EHR: “[The EHR] does not support planning intakes. [The planners] keep an Excel file with the entire planning, while you really just want to be able to do this in the EHR” (I_SS5). A final reason for this workaround is that the healthcare provider prefers a different layout than that proposed by the EHR: “People make the entire letter layout in Word because they find letters generated by the EHR ugly” (I_SS5).A reason given for using these shadow systems was the lack of overview presented by the EHR: “improvements in the system can be made” (I_SS5). On the downside, shadow systems have the expected risk for users of creating extra work when physicians have to enter information in both the EHR and their shadow system. Also, there is a risk of forgetting to register data in the EHR alongside the shadow system, resulting in the EHR system not being up-to-date.An expected benefit for the users when using shadow systems is that better overviews will be created. Also, by keeping an Excel file, the intake planners can schedule patient intakes a few months ahead, which they could not do if they only use the EHR system, thereby improving their workflow. In terms of the system, by acknowledging the deficiencies of the EHR, “What I sometimes try, and it depends a bit on the nurse to be honest, is that I say: here, you have my consent to carry it out. I will register the order later, or e-mail me at the moment that I have to order it. That's how I do it” (I_PH2). The physician continued by outlining situations in which this would happen, which are often due to inconveniences: “Imagine standing in the corridor, and you receive such a call, well, then you don’t have [the EHR] at hand. Or you’re in the middle of an outpatient visit and you are phoned. Right, it is always unexpected” (I_PH2). That is, giving verbal consent occurs because of a lack of time or physical facilities. One of the nurses offered a third reason for this workaround: “no time, no motivation. […] If they [physicians] are at home and don’t feel like starting up the system they tend to give consent verbally” (I_NU3). As such, she is implying that this workaround is related to a physician’s willingness to order through the EHR directly.The third out-system workaround identified is a physician giving verbal consent to a nurse for dispensing medication, only to also order this medication in the EHR sometime later. This workaround was not observed but mentioned during interviews with two nurses and one physician: “I think patient care is paramount, before the administrative part” (I_NU3). There is a possible risk of this workaround for the system. If one forgets to record the verbal request for medication in the EHR later, then “according to the system, this patient did not receive the medication” (I_NU3), leaving the system not up-to-date.Expected benefits of this workaround for the user are an improved workflow and time savings. For the patients, better care is expected to follow a verbal consent: The final out-system workaround entails detaching a scanner from its ‘computer-on-wheels’ (COW) to scan the wristbands of patients and the labels on infusion bags. The COW is a fairly large input/output device that forms part of the EHR.“wake up patients by the COW’s noise” (I_NU3).Nurses are supposed to bring the COW into the patient room and scan each infusion bag for every patient separately while monitoring the COW’s screen. This workaround was not observed but admitted by a nurse during an interview, who performs this workaround to not “scanning the wrong infusion bag, or the wrong patient” (I_NU3).The expected benefit of this workaround is that patients are not disturbed by the COW during the night. However, taking the scanner into the patient room and away from the COW, might jeopardize patient safety as the nurse will be unable to notice any errors that might appear on the screen when accidentally Overall, we were able to identify 11 workarounds. Two of them were in-system workflow sequence workarounds, five in-system data entry workarounds and four out-system workarounds. Each of the outlined workarounds were used by one or more occupational groups within the hospital. The expected consequences for each of these workarounds entailed benefits as well as risks and affected the user, the patient, the hospital, or the system. Table This study’s aim was to develop a typology of EHR workarounds and explore their user-perceived consequences by answering the question: what types of EHR workarounds persist and what are the user-perceived consequences? Thus, we focused on enduring rather than on temporary workarounds. These workarounds persisted for two main reasons. First, many workarounds in this hospital signal that individual physicians and medical departments have the professional autonomy to deviate from system-enforced and prescribed work processes. Doctors are ultimately accountable for patients and can, for example, prescribe medicines by telephone and have them included in the EHR system afterwards if this is in the direct medical interest of the patient. Second, EHR systems can structurally hinder desired and established work processes, requiring adaptations that the EHR supplier does not support. In the latter case, health professionals and their departments can resolve the problem by explicitly accepting and institutionalizing a workaround. Sometimes it is also necessary to first treat a patient and update the system for completeness, reimbursement, or research. From this perspective, while a workaround may be highly legitimate, especially in acute and emergency situations, it requires post-hoc data registration and processing . Below win-system workflow sequence workarounds are created in response to the time consuming and impositional characteristics of the EHR. Expected consequences of this type of workaround are benefits for the user , risks for the patient  and for the hospital (incorrect billing).We have identified three types of workarounds, two in-system and one out-system workarounds. The in-system data entry workarounds are created in response to a lack of time, fear of losing data, lack of knowledge, lack of functionality; lack of overview and the impositional features. The expected benefits for the user following this type of workaround are an improved workflow, time savings and a better overview. However, these workarounds also have foreseen risks: (1) for the user, such as additional work, loss of overview, excessive details, loss of potential system support; (2) for the patient, such as jeopardizing safety, false sense of safety and cascading errors; and (3) for the hospital, such as hindering research.Second, out-system workarounds, are responses to a lack of trust in one’s own abilities, reduced patient contact, lack of time, and limited motivation. Out-system workarounds have expected benefits for users , for patients , and for the system (improvement of the system). However, out-system workarounds also carry possible risks for the users, such extra work, loss of overview and loss of data, for the patient, such as jeopardizing safety and cascading errors, and for the system, such as the system not being up-to-date.The third type, First, the data show that users expect each type to have consequences for both users and patients. In terms of the potential benefits, users recognize six different positive consequences. An improved workflow was most often mentioned. This benefit is expected to follow from 9 of the 11 workarounds, mainly from the in-system workflow sequence and in-system data entry workarounds. As such, most of the reported workarounds are seen to contribute to an easier and more flawless workflow for users. Turning to risks, the study’s participants mentioned 11 different risks, which were more evenly distributed across the workaround types. Jeopardizing patient safety was seen as the most common risk involved. Surprisingly, the interviewed users did not acknowledge any consequences of out-system work arounds for the hospital, while they showed themselves aware of the adverse effects for the system’s integrity. Likewise, they did not think in-system workflow workarounds would affect the system, but did see risks for the hospital. Consequences for themselves and for patients may be more concrete or meaningful for them than those affecting the hospital or the system. For persisting workarounds it will be important to also systematically weigh the hospital- and system-related benefits and risks. Therefore, more research on user attributions regarding their workarounds is called for.Second, this study shows how users do not perceive their workarounds to produce only benefits or only risks. Indeed, this study reveals how seven workarounds are expected to yield both benefits and risks for the same stakeholder, mainly the users. Other workarounds are perceived to have both beneficial and risky implications but for different stakeholders. Importantly, overall the workaround types that persist remain controversial: while users expect to benefit from all workaround types, each type is also expected to create risks, especially for patients on the longer term and for research purposes.Bringing only a scanner into the patient room and Sharing login details, which were instigated by nurses and support staff. Since scanning medication, blood bags, and infusion bags is part of the nurses’ tasks  would have to be deliberate, physicians might be reluctant to acknowledge it openly.Only 2 of the 11 identified workarounds were not created by physicians. These were s’ tasks , we can s’ tasks . Since t“masters of workarounds” . A plausible explanation, given that age has been shown to play a key role in intentions regarding EHR use  I think a lot of people conduct workarounds and just think it is part of the job” (I_PH4). This may have limited the quantity and the range of workarounds presented in this paper. However, the proposed three main types seem sufficiently robust for transfer to other contexts. Second, this study was conducted at the Internal Medicine and supporting departments of a large hospital. Healthcare professionals in other departments or hospitals may create other workarounds for different reasons. Further, teaching hospitals, as this one, tend to have more elaborated EHRs [There are some limitations that may have affected this study’s results. First, while a clear set of workarounds have been identified, some may have been overlooked. It is quite possible that not all workarounds were admitted or even recognized by the interviewees. As one physician commented: ted EHRs . This coConsidering the types of workarounds, the results record only a few in-system workflow sequence workarounds compared with in-system data entry and out-system workarounds. As such, the workarounds identified in this study are not predominantly responses to perceived misalignments in the workflow. The users primarily created workarounds to deal with data registration rather than because the system was unsupportive of their workflow. This indicates that healthcare professionals deliberately work around EHR systems in order to avoid the extra administrative tasks that come with such a system, or as a form of resistance to information technology in general. This could be a relevant area for future research. Further, given the exploratory method used in this study, future research could focus on different medical specialties or on healthcare organizations other than hospitals. Finally, given the possibility that users did not voice all the workarounds they enact, we would suggest that future research on EHR workarounds employ direct and preferably relatively unobtrusive observations when examining the creation and application of workarounds, e.g. through participatory observation.This study has increased our understanding of the persistence of working around Electronic Health Records through a typology of enduring workarounds coupled with their user-perceived risks and benefits. Our typology can promote awareness among EHR users and hospital managers of the different types of workarounds and enable them to distinguish harmful from less harmful workarounds. This may support them in their decisions to prohibit, discourage or obviate the need for certain workarounds, while encouraging and possibly institutionalizing others.Additional File 1. Observation scheme. Additional File 2. Interview protocol. Additional File 3. Codebook.