Your browser doesn't support javascript.
Show: 20 | 50 | 100
Results 1 - 5 de 5
Filter
1.
Int J Popul Data Sci ; 5(4): 1393, 2021 Mar 03.
Article in English | MEDLINE | ID: covidwho-1772084

ABSTRACT

Hospital data for covid-19 surveillance, planning and modelling are challenging to find worldwide in public aggregation portals. Detailed covid-19 hospital data provides insights into covid-19's health burden including identifying which sociodemographic groups are at greatest risk of covid-19 morbidity and mortality. Timely hospital data is the best source of information for actionable forecasts and projection models of hospital capacity, including critical resources such as intensive care unit beds and ventilators that take time to plan or procure. A challenge to generate timely and detailed hospital data is the reliance on separation or discharge abstracts and census counts. What are needed are well-maintained lists of patients hospitalized with covid-19. From the standpoint of public health and health services researchers and practitioners, we describe the role of hospital data for studying covid-19, why admission data are hard to find, and how improved data infrastructure can meet surveillance and planning needs in the near future. Modern hospital electronic health records can create covid-19 patient lists and these decision support tools are increasingly used for research. These tools can generate patient lists that are transmitted and combined with public health data systems.

2.
BMJ ; 374: n2209, 2021 09 30.
Article in English | MEDLINE | ID: covidwho-1448003

ABSTRACT

OBJECTIVE: To determine if virtual care with remote automated monitoring (RAM) technology versus standard care increases days alive at home among adults discharged after non-elective surgery during the covid-19 pandemic. DESIGN: Multicentre randomised controlled trial. SETTING: 8 acute care hospitals in Canada. PARTICIPANTS: 905 adults (≥40 years) who resided in areas with mobile phone coverage and were to be discharged from hospital after non-elective surgery were randomised either to virtual care and RAM (n=451) or to standard care (n=454). 903 participants (99.8%) completed the 31 day follow-up. INTERVENTION: Participants in the experimental group received a tablet computer and RAM technology that measured blood pressure, heart rate, respiratory rate, oxygen saturation, temperature, and body weight. For 30 days the participants took daily biophysical measurements and photographs of their wound and interacted with nurses virtually. Participants in the standard care group received post-hospital discharge management according to the centre's usual care. Patients, healthcare providers, and data collectors were aware of patients' group allocations. Outcome adjudicators were blinded to group allocation. MAIN OUTCOME MEASURES: The primary outcome was days alive at home during 31 days of follow-up. The 12 secondary outcomes included acute hospital care, detection and correction of drug errors, and pain at 7, 15, and 30 days after randomisation. RESULTS: All 905 participants (mean age 63.1 years) were analysed in the groups to which they were randomised. Days alive at home during 31 days of follow-up were 29.7 in the virtual care group and 29.5 in the standard care group: relative risk 1.01 (95% confidence interval 0.99 to 1.02); absolute difference 0.2% (95% confidence interval -0.5% to 0.9%). 99 participants (22.0%) in the virtual care group and 124 (27.3%) in the standard care group required acute hospital care: relative risk 0.80 (0.64 to 1.01); absolute difference 5.3% (-0.3% to 10.9%). More participants in the virtual care group than standard care group had a drug error detected (134 (29.7%) v 25 (5.5%); absolute difference 24.2%, 19.5% to 28.9%) and a drug error corrected (absolute difference 24.4%, 19.9% to 28.9%). Fewer participants in the virtual care group than standard care group reported pain at 7, 15, and 30 days after randomisation: absolute differences 13.9% (7.4% to 20.4%), 11.9% (5.1% to 18.7%), and 9.6% (2.9% to 16.3%), respectively. Beneficial effects proved substantially larger in centres with a higher rate of care escalation. CONCLUSION: Virtual care with RAM shows promise in improving outcomes important to patients and to optimal health system function. TRIAL REGISTRATION: ClinicalTrials.gov NCT04344665.


Subject(s)
Aftercare/methods , Monitoring, Ambulatory/methods , Surgical Procedures, Operative/nursing , Telemedicine/methods , Aged , COVID-19/epidemiology , Canada/epidemiology , Female , Humans , Male , Medication Errors/statistics & numerical data , Middle Aged , Pain, Postoperative/epidemiology , Pandemics , Patient Discharge , Postoperative Period , Surgical Procedures, Operative/mortality
3.
Int J Popul Data Sci ; 5(4): 1393, 2021 Mar 03.
Article in English | MEDLINE | ID: covidwho-1175859

ABSTRACT

Hospital data for covid-19 surveillance, planning and modelling are challenging to find worldwide in public aggregation portals. Detailed covid-19 hospital data provides insights into covid-19's health burden including identifying which sociodemographic groups are at greatest risk of covid-19 morbidity and mortality. Timely hospital data is the best source of information for actionable forecasts and projection models of hospital capacity, including critical resources such as intensive care unit beds and ventilators that take time to plan or procure. A challenge to generate timely and detailed hospital data is the reliance on separation or discharge abstracts and census counts. What are needed are well-maintained lists of patients hospitalized with covid-19. From the standpoint of public health and health services researchers and practitioners, we describe the role of hospital data for studying covid-19, why admission data are hard to find, and how improved data infrastructure can meet surveillance and planning needs in the near future. Modern hospital electronic health records can create covid-19 patient lists and these decision support tools are increasingly used for research. These tools can generate patient lists that are transmitted and combined with public health data systems.

4.
CMAJ Open ; 9(1): E142-E148, 2021.
Article in English | MEDLINE | ID: covidwho-1115548

ABSTRACT

BACKGROUND: After nonelective (i.e., semiurgent, urgent and emergent) surgeries, patients discharged from hospitals are at risk of readmissions, emergency department visits or death. During the coronavirus disease 2019 (COVID-19) pandemic, we are undertaking the Post Discharge after Surgery Virtual Care with Remote Automated Monitoring Technology (PVC-RAM) trial to determine if virtual care with remote automated monitoring (RAM) compared with standard care will increase the number of days adult patients remain alive at home after being discharged following nonelective surgery. METHODS: We are conducting a randomized controlled trial in which 900 adults who are being discharged after nonelective surgery from 8 Canadian hospitals are randomly assigned to receive virtual care with RAM or standard care. Outcome adjudicators are masked to group allocations. Patients in the experimental group learn how to use the study's tablet computer and RAM technology, which will measure their vital signs. For 30 days, patients take daily biophysical measurements and complete a recovery survey. Patients interact with nurses via the cellular modem-enabled tablet, who escalate care to preassigned and available physicians if RAM measurements exceed predetermined thresholds, patients report symptoms, a medication error is identified or the nurses have concerns they cannot resolve. The primary outcome is number of days alive at home during the 30 days after randomization. INTERPRETATION: This trial will inform management of patients after discharge following surgery in the COVID-19 pandemic and offer insights for management of patients who undergo nonelective surgery in a nonpandemic setting. Knowledge dissemination will be supported through an online multimedia resource centre, policy briefs, presentations, peer-reviewed journal publications and media engagement. TRIAL REGISTRATION: ClinicalTrials.gov, no. NCT04344665.


Subject(s)
Aftercare/trends , Monitoring, Ambulatory/methods , Patient Discharge/standards , Remote Consultation/instrumentation , Adult , COVID-19/diagnosis , COVID-19/epidemiology , Canada/epidemiology , Computers, Handheld/supply & distribution , Humans , Middle Aged , Postoperative Period , SARS-CoV-2/genetics , User-Computer Interface
5.
J Gen Intern Med ; 36(1): 162-169, 2021 01.
Article in English | MEDLINE | ID: covidwho-891916

ABSTRACT

BACKGROUND: The severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) causes COVID-19 disease. There are concerns regarding limited testing capacity and the exclusion of cases from unproven screening criteria. Knowing COVID-19 risks can inform testing. This study derived and assessed a model to predict risk of SARS-CoV-2 in community-based people. METHODS: All people presenting to a community-based COVID-19 screening center answered questions regarding symptoms, possible exposure, travel, and occupation. These data were anonymously linked to SARS-CoV-2 testing results. Logistic regression was used to derive a model to predict SARS-CoV-2 infection. Bootstrap sampling evaluated the model. RESULTS: A total of 9172 consecutive people were studied. Overall infection rate was 6.2% but this varied during the study period. SARS-CoV-2 infection likelihood was primarily influenced by contact with a COVID-19 case, fever symptoms, and recent case detection rates. Internal validation found that the SARS-CoV-2 Risk Prediction Score (SCRiPS) performed well with good discrimination (c-statistic 0.736, 95%CI 0.715-0.757) and very good calibration (integrated calibration index 0.0083, 95%CI 0.0048-0.0131). Focusing testing on people whose expected SARS-CoV-2 risk equaled or exceeded the recent case detection rate would increase the number of identified SARS-CoV-2 cases by 63.1% (95%CI 54.5-72.3). CONCLUSION: The SCRiPS model accurately estimates the risk of SARS-CoV-2 infection in community-based people undergoing testing. Using SCRiPS can importantly increase SARS-CoV-2 infection identification when testing capacity is limited.


Subject(s)
COVID-19 Testing/statistics & numerical data , COVID-19/diagnosis , Risk Assessment/standards , Adolescent , Adult , Aged , Aged, 80 and over , COVID-19/epidemiology , COVID-19/transmission , Community-Acquired Infections/diagnosis , Community-Acquired Infections/epidemiology , Community-Acquired Infections/transmission , Female , Humans , Logistic Models , Male , Middle Aged , Ontario/epidemiology , Pandemics , Reverse Transcriptase Polymerase Chain Reaction , Risk Assessment/methods , SARS-CoV-2 , Surveys and Questionnaires , Young Adult
SELECTION OF CITATIONS
SEARCH DETAIL