Electronic health record-based disease surveillance systems: A systematic literature review on challenges and solutions

Health consequences of disasters: Advancing disaster data science

Understanding the health effects of disasters is critical for effective preparedness, response, recovery, and mitigation. However, research is negatively impacted by both the limited availability of disaster data and the difficulty of identifying and utilizing disaster-specific and health data sources relevant to disaster research and management. In response to numerous requests from disaster researchers, emergency managers, and operational response organizations, 73 distinct data sources at the intersection of disasters and health were compiled and categorized. These data sources generally cover the entire United States, address both disasters and health, and are available to researchers at little or no cost. Data sources are described and characterized to support improved research and guide evidence-based decision making. Current gaps and potential solutions are presented to improve disaster data collection, utilization, and dissemination.

The Development of the Municipal Registry of People with Diabetes in Porto Alegre, Brazil

Background/Objective: Diabetes registries that enhance surveillance and improve medical care are uncommon in low- and middle-income countries, where most of the diabetes burden lies. We aimed to describe the methodological and technical aspects adopted in the development of a municipal registry of people with diabetes using local and national Brazilian National Health System databases. Methods: We obtained data between July 2018 and June 2021 based on eight databases covering primary care, specialty and emergency consultations, medication dispensing, outpatient exam management, hospitalizations, and deaths. We identified diabetes using the International Classification of Disease (ICD), International Classification of Primary Care (ICPC), medications for diabetes, hospital codes for the treatment of diabetes complications, and exams for diabetes management. Results: After data processing and database merging using deterministic and probabilistic linkage, we identified 73,185 people with diabetes. Considering that 1.33 million people live in Porto Alegre, the registry captured 5.5% of the population. Conclusions: With additional data processing, the registry can reveal information on the treatment and outcomes of people with diabetes who are receiving publicly financed care in Porto Alegre. It will provide metrics for epidemiologic surveillance, such as the incidence, prevalence, rates, and trends of complications and causes of mortality; identify inadequacies; and provide information. It will enable healthcare providers to monitor the quality of care, identify inadequacies, and provide feedback as needed.

A near real-time electronic health record-based COVID-19 surveillance system: An experience from a developing country

CONTEXT

Access to real-time data that provide accurate and timely information about the status and extent of disease spread could assist management of the COVID-19 pandemic and inform decision-making.

AIM

To demonstrate our experience with regard to implementation of technical and architectural infrastructure for a near real-time electronic health record-based surveillance system for COVID-19 in Iran.

METHOD

This COVID-19 surveillance system was developed from hospital information and electronic health record (EHR) systems available in the study hospitals in conjunction with a set of open-source solutions; and designed to integrate data from multiple resources to provide near real-time access to COVID-19 patients' data, as well as a pool of health data for analytical and decision-making purposes.

OUTCOMES

Using this surveillance system, we were able to monitor confirmed and suspected cases of COVID-19 in our population and to automatically notify stakeholders. Based on aggregated data collected, this surveillance system was able to facilitate many activities, such as resource allocation for hospitals, including managing bed allocations, providing and distributing equipment and funding, and setting up isolation centres.

CONCLUSION

Electronic health record systems and an integrated data analytics infrastructure are effective tools to enable policymakers to make better decisions, and for epidemiologists to conduct improved analyses regarding COVID-19.

IMPLICATIONS

Improved quality of clinical coding for better case finding, improved quality of health information in data sources, data-sharing agreements, and increased EHR coverage in the population can empower EHR-based COVID-19 surveillance systems.

Assessing mental health from registry data: What is the best proxy?

OBJECTIVE

Medical registries frequently underestimate the prevalence of health problems compared with surveys. This study aimed to determine the registry variables that can serve as a proxy for variables studied in a mental health survey.

MATERIALS AND METHODS

Prevalences of depressive symptoms, anxiety and psychoactive medication use from the 2018 Belgian Health Interview Survey (HIS) were compared with same-year prevalences from INTEGO, a Belgian primary care registry. Participants aged 15 and above were included. We assessed correlation using Spearman's rho (SR), and agreement using the intraclass correlation coefficient (ICC). We also calculated the limits of agreement (LOAs) for each comparison. HIS questions about depressive symptoms, anxiety and psychoactive medication use were compared with the following variables from INTEGO: symptom codes, diagnosis codes, free text, antidepressant/benzodiazepine prescriptions and the combinations symptom + diagnosis codes and symptom + diagnosis codes + free text, wherever relevant.

RESULTS AND DISCUSSION

Correlation between the HIS and INTEGO was generally high, except for anxiety. Agreement ranged from fair to poor, but increased when combining certain variables, by including free text, or by increasing the prescription frequency to resemble chronic use. Agreement remained poor when comparing questions about anxiety. Prevalences from INTEGO were mostly underestimates.

CONCLUSION

The external validity of medical registries can be poor, especially compared with survey data. A considerate choice of variables and prescription chronicity is needed to accurately use a registry as a surveillance tool for mental health.

Demographics, Trends, and Clinical Characteristics of HIV Pre-Exposure Prophylaxis Recipients and People Newly Diagnosed with HIV from Large Electronic Health Records in Florida

Florida is one of the HIV epicenters with high incidence and marked sociodemographic disparities. We analyzed a decade of statewide electronic health record/claims data-OneFlorida+-to identify and characterize pre-exposure prophylaxis (PrEP) recipients and newly diagnosed HIV cases in Florida. Refined computable phenotype algorithms were applied and a total of 2186 PrEP recipients and 7305 new HIV diagnoses were identified between January 2013 and April 2021. We examined patients' sociodemographic characteristics, stratified by self-reported sex, along with both frequency-driven and expert-selected descriptions of clinical conditions documented within 12 months before the first PrEP use or HIV diagnosis. PrEP utilization rate increased in both sexes; higher rates were observed among males with sex differences widening in recent years. HIV incidence peaked in 2016 and then decreased with minimal sex differences observed. Clinical characteristics were similar between the PrEP and new HIV diagnosis cohorts, characterized by a low prevalence of sexually transmitted infections (STIs) and a high prevalence of mental health and substance use conditions. Study limitations include the overrepresentation of Medicaid recipients, with over 96% of female PrEP users on Medicaid, and the inclusion of those engaged in regular health care. Although PrEP uptake increased in Florida, and HIV incidence decreased, sex disparity among PrEP recipients remained. Screening efforts beyond individuals with documented prior STI and high-risk behavior, especially for females, including integration of mental health care with HIV counseling and testing, are crucial to further equalize PrEP access and improve HIV prevention programs.

Using Electronic Health Record System to Establish a National Patient's Registry : Lessons learned from the Cancer Registry in Iran

BACKGROUND

In Iran, the Integrated Electronic Health Record system, called SEPAS, has been established to store all patient encounters of individuals referring to healthcare facilities.

OBJECTIVE

We aimed to develop a model for cleaning SEPAS and applying its data in other databases.

METHODS

We used cancer data from SEPAS as the sample. We developed a guideline to identify codes for cancer-related diagnoses and services in the database. Furthermore, we searched the SEPAS database based on ICD-10 and the diagnosis description in English and Farsi in an Excel sheet. We added codes and descriptions of pharmaceuticals and procedures to the list. We applied the above database and linked it to the patient records to identify cancer patients. A dashboard was designed based on this information for every cancer patient.

RESULTS

We selected 5,841 diagnostic codes and phrases, 9,300 cancer pharmaceutics codes, and 452 codes from cancer-specific items related to the diagnostic procedures and treatment methods. Linkage of this list to the patient list generated a database of about 197,164 cancer patients for linkage in the registry database.

CONCLUSIONS

Patient registries are one of the most important sources of information in healthcare systems. Data linkage between Electronic Health Record Systems (EHRs) and registries, despite its challenges, is profitable. EHRs can be used for case finding in any patient registry to reduce the time and cost of case finding.

Minnesota Data Sharing May Be Model For Near-Real-Time Tracking Of Drug Overdose Hospital And ED Trends

The drug overdose epidemic in the US necessitates detailed and timely data to inform public health responses. In this article we describe how an electronic health record (EHR) data-sharing collaboration across health systems in Minnesota that was developed in response to the COVID-19 pandemic was adapted to monitor trends in substance use-related hospital and emergency department (ED) visits. We found large increases in methamphetamine- and opioid-involved hospital and ED visits. Throughout the study period, Native American, Black, and multiple-race people experienced the highest rates of drug-involved hospital and ED visits. Monitoring drug-involved health care use through EHR data has the potential to help public health officials detect trends in near real time before mortality spikes and may also inform early intervention. The use of EHR data also allows for detailed monitoring of the impact of the drug overdose epidemic across racial and ethnic groups.

Enhancing the identification of rheumatoid arthritis-associated interstitial lung disease through text mining of chest computerized tomography reports

OBJECTIVES

Algorithms have been developed to identify rheumatoid arthritis-interstitial lung disease (RA-ILD) in administrative data with positive predictive values (PPVs) between 70 and 80%. We hypothesized that including ILD-related terms identified within chest computed tomography (CT) reports through text mining would improve the PPV of these algorithms in this cross-sectional study.

METHODS

We identified a derivation cohort of possible RA-ILD cases (n = 114) using electronic health record data from a large academic medical center and performed medical record review to validate diagnoses (reference standard). ILD-related terms (e.g., ground glass, honeycomb) were identified in chest CT reports by natural language processing. Administrative algorithms including diagnostic and procedural codes as well as specialty were applied to the cohort both with and without the requirement for ILD-related terms from CT reports. We subsequently analyzed similar algorithms in an external validation cohort of 536 participants with RA.

RESULTS

The addition of ILD-related terms to RA-ILD administrative algorithms increased the PPV in both the derivation (improvement ranging from 3.6 to 11.7%) and validation cohorts (improvement 6.0 to 21.1%). This increase was greatest for less stringent algorithms. Administrative algorithms including ILD-related terms from CT reports exceeded a PPV of 90% (maximum 94.6% derivation cohort). Increases in PPV were accompanied by a decline in sensitivity (validation cohort -3.9 to -19.5%).

CONCLUSIONS

The addition of ILD-related terms identified by text mining from chest CT reports led to improvements in the PPV of RA-ILD algorithms. With high PPVs, use of these algorithms in large data sets could facilitate epidemiologic and comparative effectiveness research in RA-ILD.

Utilization of Electronic Health Records for Chronic Disease Surveillance: A Systematic Literature Review

This study reviews the current utilization of electronic health records (EHRs) for chronic disease surveillance, discusses approaches that are used in obtaining EHR-derived disease prevalence estimates, and identifies health indicators that have been studied using EHR-based surveillance methods. PubMed was searched for relevant keywords: (electronic health records [Title/Abstract] AND surveillance [Title/Abstract]) OR (electronic medical records [Title/Abstract] AND surveillance [Title/Abstract]). Articles were assessed based on detailed inclusion and exclusion criteria and organized by common themes, as per the PRISMA review protocol. The study period was limited to 2015-2021 due to the wider adoption of EHR in the U.S. only since 2015. The review included only US studies and only those that focused on chronic disease surveillance. 17 studies were included in the review. The most common approaches the review identified focused on validating EHR-derived estimates against those from traditional national surveys. The most studied conditions were diabetes, obesity, and hypertension. The majority of reviewed studies demonstrated comparable prevalence estimates with traditional population health surveillance surveys. The most common approach for the estimation of chronic disease conditions was to use small-area estimation by geographic patterns, neighborhoods, or census tracts. The use of EHR-based surveillance systems for public health purposes is feasible, and the population health estimates appear comparable to those obtained through traditional surveillance surveys. The application of EHRs for public health surveillance appears promising and could offer a real-time alternative to traditional surveillance methods. A timely assessment of population health at local and regional levels would ensure a more targeted allocation of public health and healthcare resources as well as more effective intervention and prevention initiatives.

The innovative model based on artificial intelligence algorithms to predict recurrence risk of patients with postoperative breast cancer

Purpose

This study aimed to develop a machine learning model to retrospectively study and predict the recurrence risk of breast cancer patients after surgery by extracting the clinicopathological features of tumors from unstructured clinical electronic health record (EHR) data.

Methods

This retrospective cohort included 1,841 breast cancer patients who underwent surgical treatment. To extract the principal features associated with recurrence risk, the clinical notes and histopathology reports of patients were collected and feature engineering was used. Predictive models were next conducted based on this important information. All algorithms were implemented using Python software. The accuracy of prediction models was further verified in the test cohort. The area under the curve (AUC), precision, recall, and F1 score were adopted to evaluate the performance of each model.

Results

A training cohort with 1,289 patients and a test cohort with 552 patients were recruited. From 2011 to 2019, a total of 1,841 textual reports were included. For the prediction of recurrence risk, both LSTM, XGBoost, and SVM had favorable accuracies of 0.89, 0.86, and 0.78. The AUC values of the micro-average ROC curve corresponding to LSTM, XGBoost, and SVM were 0.98 ± 0.01, 0.97 ± 0.03, and 0.92 ± 0.06. Especially the LSTM model achieved superior execution than other models. The accuracy, F1 score, macro-avg F1 score (0.87), and weighted-avg F1 score (0.89) of the LSTM model produced higher values. All P values were statistically significant. Patients in the high-risk group predicted by our model performed more resistant to DNA damage and microtubule targeting drugs than those in the intermediate-risk group. The predicted low-risk patients were not statistically significant compared with intermediate- or high-risk patients due to the small sample size (188 low-risk patients were predicted via our model, and only two of them were administered chemotherapy alone after surgery). The prognosis of patients predicted by our model was consistent with the actual follow-up records.

Conclusions

The constructed model accurately predicted the recurrence risk of breast cancer patients from EHR data and certainly evaluated the chemoresistance and prognosis of patients. Therefore, our model can help clinicians to formulate the individualized management of breast cancer patients.

Nationwide Implementation of a Population Management Dashboard for Monitoring Direct Oral Anticoagulants: Insights From the Veterans Affairs Health System

BACKGROUND

Direct oral anticoagulants are first-line therapy for common thrombotic conditions, including atrial fibrillation and venous thromboembolism. Despite their strong efficacy and safety profile, evidence-based prescribing can be challenging given differences in dosing based on indication, renal function, and drug-drug interactions. The Veterans Health Affairs developed and implemented a population management dashboard to support pharmacist review of anticoagulant prescribing. The dashboard includes information about direct oral anticoagulants and dose prescribed, renal function, age, and weight, potential interacting medications, and the need for direct oral anticoagulant medication refills. It is a stand-alone system.

METHODS

Using login data from the dashboard, nationwide implementation was evaluated using elements from the Reach, Effectiveness, Adoption, Implementation, and Maintenance framework.

RESULTS

Between August 2016 and June 2020, 150/164 sites within the Veterans Health Affairs system used the dashboard, averaging 1875 patients per site. The dashboard was made available to sites on a staggered basis. Moderate or high adoption, defined as at least one login on at least 2 separate days per month, began slowly with 3/5 sites in the pilot phase but rapidly grew to 142/150 (94.7%) sites by June 2020. The average number of unique users per site increased from 2.4 to 7.5 over the study period. Moderate to high adoption of the dashboard's use was maintained for > 6 months in 126/150 (84.0%) sites by the end of the study period.

CONCLUSIONS

There was rapid and sustained implementation and adoption of a population health dashboard for evidence-based anticoagulant prescribing across the national United States Veterans Health Administration health system. The impact of this tool on clinical outcomes and strategies to replicate this care model in other health systems will be important for broad dissemination and uptake.

Assessing the Contribution of Scanned Outside Documents to the Completeness of Real-World Data Abstraction

PURPOSE

Electronic health record (EHR) data are widely used in precision medicine, quality improvement, disease surveillance, and population health management. However, a significant amount of EHR data are stored in unstructured formats including scanned documents external to the treatment facility presenting an informatics challenge for secondary use. Studies are needed to characterize the clinical information uniquely available in scanned outside documents (SODs) to understand to what extent the availability of such information affects the use of these real-world data for cancer research.

MATERIALS AND METHODS

Two independent EHR data abstractions capturing 30 variables commonly used in oncology research were conducted for 125 patients treated for advanced non-small-cell lung cancer at a comprehensive cancer center, with and without consideration of SODs. Completeness and concordance were compared between the two abstractions, overall, and by patient groups and variable types.

RESULTS

The overall completeness of the data with SODs was 77.6% as compared with 54.3% for the abstraction without SODs. The differences in completeness were driven by data related to biomarker tests, which were more likely to be uniquely available in SODs. Such data were prone to missingness among patients who were diagnosed externally.

CONCLUSION

There were no major differences in completeness between the two abstractions by demographics, diagnosis, disease progression, performance status, or oral therapy use. However, biomarker data were more likely to be uniquely contained in the SODs. Our findings may help cancer centers prioritize the types of SOD data being abstracted for research or other secondary purposes.

Application of machine learning in predicting the risk of postpartum depression: A systematic review

BACKGROUND

Postpartum depression (PPD) presents a serious health problem among women and their families. Machine learning (ML) is a rapidly advancing field with increasing utility in predicting PPD risk. We aimed to synthesize and evaluate the quality of studies on application of ML techniques in predicting PPD risk.

METHODS

We conducted a systematic search of eight databases, identifying English and Chinese studies on ML techniques for predicting PPD risk and ML techniques with performance metrics. Quality of the studies involved was evaluated using the Prediction Model Risk of Bias Assessment Tool.

RESULTS

Seventeen studies involving 62 prediction models were included. Supervised learning was the main ML technique employed and the common ML models were support vector machine, random forest and logistic regression. Five studies (30 %) reported both internal and external validation. Two studies involved model translation, but none were tested clinically. All studies showed a high risk of bias, and more than half showed high application risk.

LIMITATIONS

Including Chinese articles slightly reduced the reproducibility of the review. Model performance was not quantitatively analyzed owing to inconsistent metrics and the absence of methods for correlation meta-analysis.

CONCLUSIONS

Researchers have paid more attention to model development than to validation, and few have focused on improvement and innovation. Models for predicting PPD risk continue to emerge. However, few have achieved the acceptable quality standards. Therefore, ML techniques for successfully predicting PPD risk are yet to be deployed in clinical environments.

Real-world data for precision public health of noncommunicable diseases: a scoping review

BACKGROUND

Global public health action to address noncommunicable diseases (NCDs) requires new approaches. NCDs are primarily prevented and managed in the community where there is little investment in digital health systems and analytics; this has created a data chasm and relatively silent burden of disease. The nascent but rapidly emerging area of precision public health offers exciting new opportunities to transform our approach to NCD prevention. Precision public health uses routinely collected real-world data on determinants of health (social, environmental, behavioural, biomedical and commercial) to inform precision decision-making, interventions and policy based on social position, equity and disease risk, and continuously monitors outcomes - the right intervention for the right population at the right time. This scoping review aims to identify global exemplars of precision public health and the data sources and methods of their aggregation/application to NCD prevention.

METHODS

The Preferred Reporting Items for Systematic Reviews and Meta-Analyses extension for scoping reviews (PRISMA-ScR) was followed. Six databases were systematically searched for articles published until February 2021. Articles were included if they described digital aggregation of real-world data and 'traditional' data for applied community, population or public health management of NCDs. Real-world data was defined as routinely collected (1) Clinical, Medication and Family History (2) Claims/Billing (3) Mobile Health (4) Environmental (5) Social media (6) Molecular profiling (7) Patient-centred (e.g., personal health record). Results were analysed descriptively and mapped according to the three horizons framework for digital health transformation.

RESULTS

Six studies were included. Studies developed population health surveillance methods and tools using diverse real-world data (e.g., electronic health records and health insurance providers) and traditional data (e.g., Census and administrative databases) for precision surveillance of 28 NCDs. Population health analytics were applied consistently with descriptive, geospatial and temporal functions. Evidence of using surveillance tools to create precision public health models of care or improve policy and practice decisions was unclear.

CONCLUSIONS

Applications of real-world data and designed data to address NCDs are emerging with greater precision. Digital transformation of the public health sector must be accelerated to create an efficient and sustainable predict-prevent healthcare system.

Precision Public Health for Non-communicable Diseases: An Emerging Strategic Roadmap and Multinational Use Cases

Non-communicable diseases (NCDs) remain the largest global public health threat. The emerging field of precision public health (PPH) offers a transformative opportunity to capitalize on digital health data to create an agile, responsive and data-driven public health system to actively prevent NCDs. Using learnings from digital health, our aim is to propose a vision toward PPH for NCDs across three horizons of digital health transformation: Horizon 1—digital public health workflows; Horizon 2—population health data and analytics; Horizon 3—precision public health. This perspective provides a high-level strategic roadmap for public health practitioners and policymakers, health system stakeholders and researchers to achieving PPH for NCDs. Two multinational use cases are presented to contextualize our roadmap in pragmatic action: ESP and RiskScape (USA), a mature PPH platform for multiple NCDs, and PopHQ (Australia), a proof-of-concept population health informatics tool to monitor and prevent obesity. Our intent is to provide a strategic foundation to guide new health policy, investment and research in the rapidly emerging but nascent area of PPH to reduce the public health burden of NCDs.

Minnesota Electronic Health Record Consortium COVID-19 Project: Informing Pandemic Response Through Statewide Collaboration Using Observational Data

OBJECTIVE

Robust disease and syndromic surveillance tools are underdeveloped in the United States, as evidenced by limitations and heterogeneity in sociodemographic data collection throughout the COVID-19 pandemic. To monitor the COVID-19 pandemic in Minnesota, we developed a federated data network in March 2020 using electronic health record (EHR) data from 8 multispecialty health systems.

MATERIALS AND METHODS

In this serial cross-sectional study, we examined patients of all ages who received a COVID-19 polymerase chain reaction test, had symptoms of a viral illness, or received an influenza test from January 3, 2016, through November 7, 2020. We evaluated COVID-19 testing rates among patients with symptoms of viral illness and percentage positivity among all patients tested, in aggregate and by zip code. We stratified results by patient and area-level characteristics.

RESULTS

Cumulative COVID-19 positivity rates were similar for people aged 12-64 years (range, 15.1%-17.6%) but lower for adults aged ≥65 years (range, 9.3%-10.7%). We found notable racial and ethnic disparities in positivity rates early in the pandemic, whereas COVID-19 positivity was similarly elevated across most racial and ethnic groups by the end of 2020. Positivity rates remained substantially higher among Hispanic patients compared with other racial and ethnic groups throughout the study period. We found similar trends across area-level income and rurality, with disparities early in the pandemic converging over time.

PRACTICE IMPLICATIONS

We rapidly developed a distributed data network across Minnesota to monitor the COVID-19 pandemic. Our findings highlight the utility of using EHR data to monitor the current pandemic as well as future public health priorities. Building partnerships with public health agencies can help ensure data streams are flexible and tailored to meet the changing needs of decision makers.

Critical Periods, Critical Time Points and Day-of-the-Week Effects in COVID-19 Surveillance Data: An Example in Middlesex County, Massachusetts, USA

Critical temporal changes such as weekly fluctuations in surveillance systems often reflect changes in laboratory testing capacity, access to testing or healthcare facilities, or testing preferences. Many studies have noted but few have described day-of-the-week (DoW) effects in SARS-CoV-2 surveillance over the major waves of the novel coronavirus 2019 pandemic (COVID-19). We examined DoW effects by non-pharmaceutical intervention phases adjusting for wave-specific signatures using the John Hopkins University’s (JHU’s) Center for Systems Science and Engineering (CSSE) COVID-19 data repository from 2 March 2020 through 7 November 2021 in Middlesex County, Massachusetts, USA. We cross-referenced JHU’s data with Massachusetts Department of Public Health (MDPH) COVID-19 records to reconcile inconsistent reporting. We created a calendar of statewide non-pharmaceutical intervention phases and defined the critical periods and timepoints of outbreak signatures for reported tests, cases, and deaths using Kolmogorov-Zurbenko adaptive filters. We determined that daily death counts had no DoW effects; tests were twice as likely to be reported on weekdays than weekends with decreasing effect sizes across intervention phases. Cases were also twice as likely to be reported on Tuesdays-Fridays (RR = 1.90–2.69 [95%CI: 1.38–4.08]) in the most stringent phases and half as likely to be reported on Mondays and Tuesdays (RR = 0.51–0.93 [0.44, 0.97]) in less stringent phases compared to Sundays; indicating temporal changes in laboratory testing practices and use of healthcare facilities. Understanding the DoW effects in daily surveillance records is valuable to better anticipate fluctuations in SARS-CoV-2 testing and manage appropriate workflow. We encourage health authorities to establish standardized reporting protocols.

Emergency teleradiological activity is an epidemiological estimator and predictor of the covid-19 pandemic in mainland France

Background

COVID-19 pandemic highlighted the need for real-time monitoring of diseases evolution to rapidly adapt restrictive measures. This prospective multicentric study aimed at investigating radiological markers of COVID-19-related emergency activity as global estimators of pandemic evolution in France. We incorporated two sources of data from March to November 2020: an open-source epidemiological dataset, collecting daily hospitalisations, intensive care unit admissions, hospital deaths and discharges, and a teleradiology dataset corresponding to the weekly number of CT-scans performed in 65 emergency centres and interpreted remotely. CT-scans specifically requested for COVID-19 suspicion were monitored. Teleradiological and epidemiological time series were aligned. Their relationships were estimated through a cross-correlation function, and their extremes and breakpoints were compared. Dynamic linear models were trained to forecast the weekly hospitalisations based on teleradiological activity predictors.

Results

A total of 100,018 CT-scans were included over 36 weeks, and 19,133 (19%) performed within the COVID-19 workflow. Concomitantly, 227,677 hospitalisations were reported. Teleradiological and epidemiological time series were almost perfectly superimposed (cross-correlation coefficients at lag 0: 0.90–0.92). Maximal number of COVID-19 CT-scans was reached the week of 2020-03-23 (1 086 CT-scans), 1 week before the highest hospitalisations (23,542 patients). The best valid forecasting model combined the number of COVID-19 CT-scans and the number of hospitalisations during the prior two weeks and provided the lowest mean absolute percentage (5.09%, testing period: 2020-11-02 to 2020-11-29).

Conclusion

Monitoring COVID-19 CT-scan activity in emergencies accurately and instantly predicts hospitalisations and helps adjust medical resources, paving the way for complementary public health indicators.

Supplementary Information

The online version contains supplementary material available at 10.1186/s13244-021-01040-3.

Lessons learned from hemolytic uremic syndrome registries: recommendations for implementation

BACKGROUND

Hemolytic uremic syndrome (HUS) is a rare condition which diagnosed with the triad of thrombocytopenia, microangiopathic hemolytic anemia, and acute renal injury. There is a high requirement for research to discover treatments. HUS registries can be used as an important information infrastructure. In this study, we identified and compared the different features of HUS registries to present a guide for the development and implementation of HUS registries.

RESULTS

The purposes of registries were classified as clinical (9 registries), research (7 registries), and epidemiological (5 registries), and only 3 registries pursued all three types of purposes. The data set included demographic data, medical and family history, para-clinical and diagnostic measures, treatment and pharmacological data, complications, and outcomes. The assessment strategies of data quality included monthly evaluation and data audit, the participation of physicians to collect data, editing and correcting data errors, increasing the rate of data completion, following guidelines and data quality training, using specific data quality indicators, and real-time evaluation of data at the time of data entry. 8 registries include atypical HUS patients, and 7 registries include all patients regardless of age. Only two registries focused on children. 4 registries apply prospective and 4 applied both prospective, and retrospective data collection. Finally, specialized hospitals were the main data source for these registries.

CONCLUSION

Based on the findings, we suggested a learning framework for developing and implementing an HUS registry. This framework includes lessons learned and suggestions for HUS registry purposes, minimum data set, data quality assurance, data collection methods, inclusion and exclusion criteria as well as data sources. This framework can help researchers develop HUS registries.

Regional COVID-19 registry in Khuzestan, Iran: A study protocol and lessons learned from a pilot implementation

Disease registry systems provide a strong information infrastructure for decision-making and research. The purpose of this study is to describe the implementation method and protocol of the COVID-19 registry in Khuzestan province, Iran. We established a steering committee and formulated the purposes of the registry. Then, based on reviewing the literature, and expert panels, the minimum data set, the data collection forms and the web-based software were developed. Data collection is done retrospectively through Hospital Information Systems, Medical Care Monitoring Center system (MCMC), Management of Communicable Disease Prevention and Control system (MCDPC) as well as, patients' records. For prospective data collection, the data collection forms are compiled with patients' medical records by the medical staff and are then entered into the registry system. We collect patients' administrative and demographic data, history and physical examinations, test and imaging results, disease progression, treatment, outcomes, and follow-ups of the confirmed and suspected inpatients and outpatients. From April 20 to December 5, 2020, the data of 4,812 confirmed cases and 7,113 suspected cases were collected from two COVID-19 referral hospitals. Based on our experience, recording information along with providing care for patients and putting patients' data registration in the medical staff's routine, structuring data, having a flexible technical team and rapid software development for multiple and continuous updates, automating data collection by connecting the registry to existing information systems and having different incentives, the registration process can be strengthened.

Trends in Pediatric Viral Symptoms, Influenza Testing, and SARS-CoV-2 Testing From a Statewide Electronic Health Record Consortium, January 2017 to July 2021

OBJECTIVE

The heterogeneous implementation and uptake of nonpharmaceutical interventions (NPIs) during the coronavirus disease 2019 (COVID-19) pandemic amplified the need for locally responsive disease surveillance mechanisms. Using data from a newly developed statewide electronic health record (EHR) consortium in Minnesota, we sought to characterize trends in pediatric viral symptoms, influenza testing, and severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) testing.

METHODS

We conducted a serial cross-sectional analysis of EHR data from 1/1/2017 to 7/30/2021 across 8 large health systems in Minnesota. We included patients ≤18 years of age with any SARS-CoV-2 test, influenza test, or documented diagnostic code which met our viral symptom definition. We plotted week-by-week trends in viral symptoms, SARS-CoV-2 and influenza testing, and test positivity, stratified between children (0-11 years) and adolescents (12-18 years).

RESULTS

We identified 1,079,924 patients ≤18 years of age with viral symptoms or testing; 880,669 (81.5%) were children ≤11 years. Influenza testing and influenza test positivity remained well below historical averages from March 2020 through mid-May 2021. Peaks in viral symptoms during this time were concomitant with peaks in SARS-CoV-2 testing and test positivity, whereas influenza testing and test positivity remained stagnant. Influenza test positivity rates increased substantively among children from May through July 2021.

CONCLUSIONS

Viral illness and influenza testing among pediatric patients were below historical averages throughout the COVID-19 pandemic. Ongoing increases in influenza test positivity may merit clinical and public health awareness and intervention. Future NPI policies can be better targeted with insights from collaborative EHR-based surveillance, which enhances real-time, locally sensitive measurement of disease outbreaks.