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Angel | Alive

Total Raised




Last Raised

$1.12M | 5 yrs ago

About Doctaly

BDM Medical, dba Doctaly, is a health-tech marketplace that brings on-demand, affordable, face-to-face general practice (GP) appointments to patients.

Doctaly Headquarters Location

Fifth Floor 11 Leadenhall St

London, England, EC3V 1LP,

United Kingdom

0800 788 0898

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Expert Collections containing Doctaly

Expert Collections are analyst-curated lists that highlight the companies you need to know in the most important technology spaces.

Doctaly is included in 1 Expert Collection, including Digital Health.


Digital Health

13,114 items

Technologies, platforms, and systems that engage consumers for lifestyle, wellness, or health-related purposes; capture, store, or transmit health data; and/or support life science and clinical operations. (DiME, DTA, HealthXL, & NODE.Health)

Latest Doctaly News

Predicting Risk of Hospital Admission in Patients With Suspected COVID-19 in a Community Setting: Protocol for Development and Validation of a Multivariate Risk Prediction Tool

May 25, 2021

March 24, 2021 . Predicting Risk of Hospital Admission in Patients With Suspected COVID-19 in a Community Setting: Protocol for Development and Validation of a Multivariate Risk Prediction Tool Predicting Risk of Hospital Admission in Patients With Suspected COVID-19 in a Community Setting: Protocol for Development and Validation of a Multivariate Risk Prediction Tool Authors of this article: 2Patient Safety Translational Research Centre, Institute of Global Health Innovation, Imperial College London, London, United Kingdom 3Center for Health Technology and Services Research / Department of Community Medicine, Health Information and Decision (CINTESIS/MEDCIDS), Faculty of Medicine, University of Porto, Porto, Portugal 4Nuffield Department of Primary Care Health Sciences, University of Oxford, Oxford, United Kingdom 5Whole Systems Integrated Care, North West London Clinical Commissioning Group, London, United Kingdom Corresponding Author: Imperial College London Abstract Background: During the pandemic, remote consultations have become the norm for assessing patients with signs and symptoms of COVID-19 to decrease the risk of transmission. This has intensified the clinical uncertainty already experienced by primary care clinicians when assessing patients with suspected COVID-19 and has prompted the use of risk prediction scores, such as the National Early Warning Score (NEWS2), to assess severity and guide treatment. However, the risk prediction tools available have not been validated in a community setting and are not designed to capture the idiosyncrasies of COVID-19 infection. Objective: The objective of this study is to produce a multivariate risk prediction tool, RECAP-V1 (Remote COVID-19 Assessment in Primary Care), to support primary care clinicians in the identification of those patients with COVID-19 that are at higher risk of deterioration and facilitate the early escalation of their treatment with the aim of improving patient outcomes. Methods: The study follows a prospective cohort observational design, whereby patients presenting in primary care with signs and symptoms suggestive of COVID-19 will be followed and their data linked to hospital outcomes (hospital admission and death). Data collection will be carried out by primary care clinicians in four arms: North West London Clinical Commissioning Groups (NWL CCGs), Oxford-Royal College of General Practitioners (RCGP) Research and Surveillance Centre (RSC), Covid Clinical Assessment Service (CCAS), and South East London CCGs (Doctaly platform). The study involves the use of an electronic template that incorporates a list of items (known as RECAP-V0) thought to be associated with disease outcome according to previous qualitative work. Data collected will be linked to patient outcomes in highly secure environments. We will then use multivariate logistic regression analyses for model development and validation. Results: Recruitment of participants started in October 2020. Initially, only the NWL CCGs and RCGP RSC arms were active. As of March 24, 2021, we have recruited a combined sample of 3827 participants in these two arms. CCAS and Doctaly joined the study in February 2021, with CCAS starting the recruitment process on March 15, 2021. The first part of the analysis (RECAP-V1 model development) is planned to start in April 2021 using the first half of the NWL CCGs and RCGP RSC combined data set. Posteriorly, the model will be validated with the rest of the NWL CCGs and RCGP RSC data as well as the CCAS and Doctaly data sets. The study was approved by the Research Ethics Committee on May 27, 2020 (Integrated Research Application System number: 283024, Research Ethics Committee reference number: 20/NW/0266) and badged as National Institute of Health Research Urgent Public Health Study on October 14, 2020. Conclusions: We believe the validated RECAP-V1 early warning score will be a valuable tool for the assessment of severity in patients with suspected COVID-19 in the community, either in face-to-face or remote consultations, and will facilitate the timely escalation of treatment with the potential to improve patient outcomes. Trial Registration: ISRCTN registry ISRCTN13953727; International Registered Report Identifier (IRRID): DERR1-10.2196/29072 JMIR Res Protoc 2021;10(5):e29072 Overview During 2020, it became clear that assessment of the severity of COVID-19 infection required clinical tools specific to the condition and that repurposing tools such as the National Early Warning Score (NEWS2), designed for the early diagnosis of sepsis, would not be safe clinical practice [ 1 ]. The management of COVID-19 by clinicians is challenged by uncertainty about the disease progression [ 2 ]. There is evidence that a small percentage of patients present a dramatic deterioration of clinical status around the 8th to 10th day of disease, often associated with unperceived low oxygen saturations (known as “silent hypoxia”) that may require hospital and intensive care unit (ICU) admissions [ 3 - 5 ]. The inability to predict which patients will experience clinical deterioration adds an additional level of complexity to the clinical challenge and diagnostic uncertainty that general practitioners (GPs) have faced during the pandemic, particularly as most of the consultations are carried out remotely (usually by telephone and occasionally by video) to minimize the risk of transmission [ 6 ]. It was initially suggested that NEWS2 could be used to assess severity of patients with COVID-19 [ 7 ]. NEWS2 is calculated from patient’s temperature, pulse rate, respiratory rate, systolic blood pressure, pulse oximetry reading, and presence of new onset of acute confusion [ 8 ]. It is commonly used in hospital settings and ambulance service prior to transfer to hospital to assess the risk of deterioration of a patient [ 9 ]. However, NEWS2 seems to be a late indicator of decompensation, typically triggering within the last 12 hours before a transfer to ICU is considered necessary and, therefore, this limits its application and validity in a primary care or community care setting where an earlier warning would be preferred [ 9 , 10 ]. The Roth score (originally developed as a measure of breathlessness in cardiopulmonary disease [ 11 ]) was briefly considered by the Royal College of General Practitioners (RCGP) as possibly useful in the assessment of breathlessness when assessing patients with signs and symptoms of COVID-19 [ 12 ]. However, a rapid literature review concluded that the Roth score might have a low sensitivity (ie, a normal score in patients with “silent hypoxia”), and therefore should not be used by GPs when assessing patients over the phone or in video consultations [ 13 ]. Justification and Study Objective This new condition and the forced shift toward remote consultations during the pandemic have increased the challenges and uncertainty commonly faced in general practice [ 6 ]. Primary care clinicians need a tool to guide the management of patients with suspected COVID-19 to be able to identify those whom they can reassure, those that need monitoring, and those that require urgent further assessment or referral to hospital. Even though the validity of NEWS2 for this purpose was a subject of intense debate during the height of the first COVID-19 wave, the score is still being used by primary care clinicians to assess patients prior to transfer to hospital [ 9 ]. The use of NEWS2 outside the hospital setting has not been validated, and it was not designed to capture the idiosyncrasies of COVID-19 infection. Therefore, there is need to develop an early warning score that incorporates key features of acute COVID-19 and that can be safely used by GPs when assessing patients remotely [ 14 ]. We reviewed the literature on COVID-19 early warning scores, then conducted a series of focus groups with 72 primary care clinicians (mostly GPs and including advanced nurse practitioners and paramedics) to derive elements that might form part of a suitable score, value sets, and appropriate SNOMED terms [ 15 ]. This paper describes the process of quantitative development and validation of the Remote COVID-19 Assessment in Primary Care (RECAP) score. The objective was to produce a multivariate risk prediction tool to facilitate the early identification, by primary care physicians and other clinicians working in the community, of those patients with COVID-19 that are at higher risk of becoming severely ill and inform the early escalation of their treatment, while also reducing unnecessary referrals in low-risk patients, with the aim of improving patient outcomes. Methods Study Design This primary care data linkage study follows a prospective cohort observational design, whereby patients presenting in primary or community care with signs and symptoms suggestive of COVID-19 will be followed and their data linked with hospital outcomes, particularly focusing on hospital admission, ICU admission, and death. For data collection purposes, the initial set of items identified in earlier qualitative work [ 15 ], known as RECAP-V0, will be integrated into an electronic template to be used by primary care physicians (see Figure 1 for a summary of items included in RECAP-V0). This will enable the standardized recording of patients’ signs and symptoms and subsequent linkage with hospital and mortality data. Data collected will be used to develop and validate a multivariate regression model to predict hospital admission, ICU admission, and death. ‎ Recruitment The development of the RECAP score will require the use of primary and secondary data. The collection of patients’ signs and symptoms as they present in primary care requires the involvement of primary care clinicians, who will be asked to assess those patients with a clinical diagnosis of suspected COVID-19 using the RECAP electronic template. The recruitment of clinicians (study sites) and patients (study participants) will be carried out by four different arms depending on clinician and participant location and service used to seek medical care: North West London (NWL) Clinical Commissioning Groups (CCGs) arm: this arm has its own integrated linked database (Whole Systems Integrated Care [WSIC]) and a secure environment (Imperial’s Clinical Analytics, Research and Evaluation [iCARE] secure environment) to hold the data. Recruitment of practices will be facilitated by the NWL clinical research network (CRN). General practitioners will use EMIS [ 16 ] or TPP SystmOne [ 17 ] electronic health record systems to capture patients’ data. RCGP Research and Surveillance Centre (RSC) arm: this is a national network of practices within the RCGP developed to contribute with data for disease surveillance and research [ 18 ], which is held in the Oxford RCGP Clinical Informatics Digital Hub (ORCHID) secure environment [ 19 ]. Subject to the patient’s consent, data from RSC network practices (collected from computerized medical record systems EMIS or TPP SystmOne, the United Kingdom’s most used systems, using Ardens RECAP electronic templates [ 20 ]) will be pseudonymized and extracted via a Wellbeing Software extraction system and linked to outcomes. Covid Clinical Assessment Service (CCAS) arm: this service is organized within the National Health Service (NHS) 111 Online service (managed by South Central Ambulance Service) for the clinical assessment and management of patients with a clinical diagnosis of suspected COVID-19. It is staffed by general practitioners and uses the Adastra electronic health record system [ 21 ]. Upon patients’ consent, the data collected will be transferred to ORCHID and linked to hospital outcomes. Doctaly arm: this private health care platform has been commissioned by South East London CCGs to provide a home monitoring service for patients with a diagnosis of COVID-19 (positive result in laboratory test) in South East London. Patients’ medical history and assessment data are collected using a chatbot via the WhatsApp mobile app. The questions asked via the Doctaly chatbot were designed to reflect the same concepts as the RECAP-V0 set. Data collected will be also transferred to the Oxford secure environment and linked to outcome data. Figure 2 below depicts study data sources and data flow. Primary care data collected by practices in NWL and held in iCARE are already linked to hospital outcomes (ie, hospital admission, ICU admission, and death). Data held in the University of Oxford secure environment (RCGP RSC, CCAS, and Doctaly data) will be linked to outcome data contained in the Hospital Episode Statistics (HES) and Office of National Statistics (ONS) databases using an encrypted NHS number. Hospital admission and mortality data are available in HES and ONS; however, ICU admission information is not available. ‎ Figure 2. Data flowchart. CCG: Clinical Commissioning Group; NHS: National Health Service; NWL: North West London; ORCHID: Oxford RCGP Clinical Informatics Digital Hub; RCGP: Royal College of General Practitioners; RSC: Research and Surveillance Centre; SE: South East. Selection Criteria Our main cohort includes patients clinically diagnosed with COVID-19 that are being assessed and managed in primary care. Additional cohorts include patients with signs and symptoms suggestive of COVID-19 assessed by the NHS 111 CCAS and patients with established COVID-19 that are assessed as part of a primary care–led home monitoring service (Doctaly). In the NWL, RCGP RSC, and CCAS arms, participants will be identified by primary care clinicians and enrolled in the study if they satisfy the following inclusion criteria: The patient is willing and able to provide informed consent for data linkage (exceptions are described in detail in the Overview section of the Results) The patient has signs and symptoms that are judged by the clinician to be suggestive of acute COVID-19 and time since onset of symptoms is ≤14 days. The participant is 18 years of age or older. The clinician is able to use the electronic template that contains the RECAP codes. Data collected by the clinician can be linked to the following hospital outcomes: hospital admission, ICU admission (only for NWL CCGs arm data), and hospital outcome (either discharge or cause of death). For data collected in South East London CCGs (Doctaly) arm, the selection criteria consist of participant age (ie, 18 years old or older) and having a data sharing or consent procedure in place, since the other criteria are already satisfied (ie, patients are offered home monitoring after receiving a positive result from a COVID-19 test and the monitoring tool was specifically designed to include RECAP codes). Template Development In order to collect primary data from primary care or community care settings, the RECAP-V0 items that captured patients’ signs and symptoms along with other characteristics (sociodemographic information and comorbidities) are transferred into an electronic template using SNOMED and Read codes. These codes have been identified by the study team and collaborators and have been reviewed by NHSX, NHS England, and the UK Faculty of Clinical Informatics. The templates have been deployed for COVID-19 management via electronic health record systems—such as Ardens EMIS and SystmOne, TPP SystmOne, or Adastra—used by clinicians in GP practices, COVID-19 hubs, and CCAS, or via the patient-facing platform Doctaly. This will enable the collection of patients’ signs and symptoms in large data sets that will be stored in two secure environments (ORCHID and iCARE secure environments). Sample Size A total of 2880 participants will be necessary to develop a model with a minimum 85% specificity, assuming 10% prevalence of hospital admission and 6% missing data. We will split the sample into two consecutive groups, taking the first 50% of participants’ data for model development and the last 50% for model validation. CCAS will also collect 2880 participants as we wish to explore the hypothesis that, on account of case mix and spectrum bias, patients already triaged to the national service may require a separate model. We will then separately develop and validate a model for CCAS. Doctaly will provide an additional validation data set for RECAP-V1 score. Data Analysis Overview A detailed statistical analysis plan (SAP) written before inspecting the data will be followed for analysis. The SAP provides a detailed description of data handling, RECAP-V1 model development and validation, and any planned secondary outcomes analysis. Given the complexity of issues to be addressed, including missing data not at random, potential correlations between clinical measurements; regression models and machine learning; and the relationships between the four different data sets, the SAP will be the subject of a separate article. RECAP-V1 Early Warning Score Development and Validation We will use multivariate logistic regressions to develop and validate the score. Table 1 contains a list of the items we included in the RECAP-V0 electronic template along with their SNOMED codes that will be used as inputs in the model. The template has been designed to support the assessment of patients via both face-to-face and remote consultations; however, we anticipate that there are certain observations, such as respiratory rate or oxygen saturation, whose recording in remote consultations may be challenging. Therefore, we included information on patients’ symptoms that could be used as a proxy of quantitative items if they were unavailable. The factors for the model (predictor variables) can then be summarized as follows: heart rate, respiratory rate or shortness of breath, trajectory of breathlessness, oxygen saturation or level of tiredness, temperature or feeling feverish, days from onset of symptoms, muscle aches, and cognitive decline. Moreover, we will extract other patient characteristics such as age, gender, body mass index, ethnicity, presence of comorbidities (eg, diabetes, hypertension, coronary heart disease, and chronic kidney disease), and whether the patient is or has been on a COVID-19 shielding list. During the conduct of the study, the QCOVID score [ 22 ] has been adopted as a measure of baseline risk and used to populate the COVID-19 shielding list in health record systems [ 23 ]. We expect that patient characteristics ought to be able to be represented by the shielding term and will test this hypothesis. Missing data will be handled with standard methodologies for the multiple imputation of missing data [ 24 ]. Regarding the outputs of the model, we are interested in hospital admission (defined as an overnight hospital stay within 28 days of onset of symptoms), ICU admission (only available in NWL’s WSIC/iCARE database), and death (either at the hospital or at home within 28 days of onset of symptoms). We will also conduct exploratory analyses, using machine learning algorithms for outcome prediction (nonlinear classifiers) including random forest, gradient boosting, and neural networks, alongside machine learning approaches for imputation of missing data. Table 1. RECAP-V0 template items. Variable name

  • Where is Doctaly's headquarters?

    Doctaly's headquarters is located at Fifth Floor, London.

  • What is Doctaly's latest funding round?

    Doctaly's latest funding round is Angel.

  • How much did Doctaly raise?

    Doctaly raised a total of $1.12M.

  • Who are the investors of Doctaly?

    Investors of Doctaly include Crowdcube.

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