The completeness of electronic medical record data for patients with Type 2 Diabetes in primary care and its implications for computer modelling of predicted clinical outcomes

Healthcare in England was affected by the COVID-19 pandemic across the pancreatic cancer pathway: A cohort study using OpenSAFELY-TPP

Background

Healthcare across all sectors, in the UK and globally, was negatively affected by the COVID-19 pandemic. We analysed healthcare services delivered to people with pancreatic cancer from January 2015 to March 2023 to investigate the effect of the COVID-19 pandemic.

Methods

With the approval of NHS England, and drawing from a nationally representative OpenSAFELY-TPP dataset of 24 million patients (over 40% of the English population), we undertook a cohort study of people diagnosed with pancreatic cancer. We queried electronic healthcare records for information on the provision of healthcare services across the pancreatic cancer pathway. To estimate the effect of the COVID-19 pandemic, we predicted the rates of healthcare services if the pandemic had not happened. We used generalised linear models and the pre-pandemic data from January 2015 to February 2020 to predict rates in March 2020 to March 2023. The 95% confidence intervals of the predicted values were used to estimate the significance of the difference between the predicted and observed rates.

Results

The rate of pancreatic cancer and diabetes diagnoses in the cohort was not affected by the pandemic. There were 26,840 people diagnosed with pancreatic cancer from January 2015 to March 2023. The mean age at diagnosis was 72 (±11 SD), 48% of people were female, 95% were of White ethnicity, and 40% were diagnosed with diabetes. We found a reduction in surgical resections by 25-28% during the pandemic. In addition, 20%, 10%, and 4% fewer people received body mass index, glycated haemoglobin, and liver function tests, respectively, before they were diagnosed with pancreatic cancer. There was no impact of the pandemic on the number of people making contact with primary care, but the number of contacts increased on average by 1-2 per person amongst those who made contact. Reporting of jaundice decreased by 28%, but recovered within 12 months into the pandemic. Emergency department visits, hospital admissions, and deaths were not affected.

Conclusions

The pandemic affected healthcare in England across the pancreatic cancer pathway. Positive lessons could be learnt from the services that were resilient and those that recovered quickly. The reductions in healthcare experienced by people with cancer have the potential to lead to worse outcomes. Current efforts should focus on addressing the unmet needs of people with cancer.

Funding

This work was jointly funded by the Wellcome Trust (222097/Z/20/Z); MRC (MR/V015757/1, MC_PC-20059, MR/W016729/1); NIHR (NIHR135559, COV-LT2-0073), and Health Data Research UK (HDRUK2021.000, 2021.0157). This work was funded by Medical Research Council (MRC) grant reference MR/W021390/1 as part of the postdoctoral fellowship awarded to AL and undertaken at the Bennett Institute, University of Oxford. The views expressed are those of the authors and not necessarily those of the NIHR, NHS England, UK Health Security Agency (UKHSA), or the Department of Health and Social Care. Funders had no role in the study design, collection, analysis, and interpretation of data; in the writing of the report; and in the decision to submit the article for publication.

Blood pressure and cholesterol measurements in primary care: cross-sectional analyses in a dynamic cohort

Background

Guidelines on cardiovascular risk management (CVRM) recommend blood pressure (BP) and cholesterol measurements every 5 years in men aged ≥40 years and (post-menopausal) women aged ≥50 years.

Aim

To evaluate CVRM guideline implementation.

Design & setting

Cross-sectional analyses in a dynamic cohort using primary care electronic health record (EHR) data from the Julius General Practitioners’ Network (JGPN) (n = 388 929).

Method

Trends (2008–2018) were assessed in the proportion of patients with at least one measurement (BP and cholesterol) every 1, 2, and 5 years, in those with:

1. a history of cardiovascular disease (CVD) and diabetes mellitus (DM);

2. a history of DM only;

3. a history of CVD only;

4. a cardiovascular risk assessment (CRA) indication based on other medical history, or;

5. no CRA indication.

Trends were evaluated over time using logistic regression mixed-model analyses.

Results

Trends in annual BP and cholesterol measurement increased for patients with a history of CVD from 37.0% to 48.4% (P<0.001) and 25.8% to 40.2% (P<0.001). In the 5-year window from 2014–2018, BP and cholesterol measurements were performed respectively in 78.5% and 74.1% of all men aged ≥40 years and 82.2% and 78.5% of all women aged ≥50 years. Least measured were patients without a CRA indication (men 60.2% and 62.4%; women 55.5% and 59.3%).

Conclusion

The fairly high frequency of CVRM measurements available in the EHR of patients in primary care suggests an adequate implementation of the CVRM guideline. As nearly all individuals visit the GP at least once within a 5-year time window, improvement of CVRM remains possible, especially in those without a CRA indication.

A longitudinal follow-up study of a type 2 diabetes "lost to follow-up" cohort - positive effect on glycaemic control after changes in medication

The aim of this study was to evaluate whether patients with type 2 diabetes (T2D) who had stopped attending their diabetes treatment system (referred to as "lost to follow-up", LTF) but who succeeded in improving their glycaemic control after returning to the diabetes treatment system had changes in their diabetes medication when compared with similar patients who did not show improvement. "LTFs" who had baseline haemoglobin A_1 c (HbA_1 c) ≥53 mmol/mol and succeeded in reducing HbA_1 c ≥ 6 mmol/mol during a 12-30 month follow-up period after adhering again to their diabetes treatment system were compared with "LTFs" who had an unsatisfactory change in HbA_1 c or with "LTFs" who maintained good glycaemic control throughout the 12-30 month follow-up period. Unsatisfactory change in HbA_1 c was determined as HbA_1 c ≥ 53 mmol/mol and change <6 mmol/mol after the 12-30 month follow-up period in their diabetes treatment system or HbA_1 c < 53 mmol/mol when returning to the diabetes treatment system but ≥53 mmol/mol at the end of the 12-30 month follow-up period. "LTFs" with improvement in glycaemic control used a higher number of different anti-hyperglycaemic agents (P < 0.001) and their dosages of metformin increased (P < 0.05) when compared with "LTFs" without improvement or "LTFs" with satisfactory glycaemic control. Cholesterol-, LDL-cholesterol- and triglyceride-concentrations decreased during the 12-30 month follow-up period (P < 0.05) in "LTFs" with improved glycaemic control, but not in the other groups. "LTFs" with T2D who had poor glycaemic control seemed to require an increase in their anti-diabetic medication when attempting to improve their glycaemic control.

Limitations for health research with restricted data collection from UK primary care

Purpose

UK primary care provides a rich data source for research. The impact of proposed data collection restrictions is unknown. This study aimed to assess the impact of restricting the scope of electronic health record (EHR) data collection on the ability to conduct research. The study estimated the consequences of restricted data collection on published Clinical Practice Research Datalink studies from high impact journals or referenced in clinical guidelines.

Methods

A structured form was used to systematically analyse the extent to which individual studies would have been possible using a database with data collection restrictions in place: (1) retrospective collection of specified diseases only; (2) retrospective collection restricted to a 6‐ or 12‐year period; (3) prospective and retrospective collection restricted to non‐sensitive data. Outcomes were categorised as unfeasible (not reproducible without major bias); compromised (feasible with design modification); or unaffected.

Results

Overall, 91% studies were compromised with all restrictions in place; 56% studies were unfeasible even with design modification. With restrictions on diseases alone, 74% studies were compromised; 51% were unfeasible. Restricting collection to 6/12 years had a major impact, with 67 and 22% of studies compromised, respectively. Restricting collection of sensitive data had a lesser but marked impact with 10% studies compromised.

Conclusion

EHR data collection restrictions can profoundly reduce the capacity for public health research that underpins evidence‐based medicine and clinical guidance. National initiatives seeking to collect EHRs should consider the implications of restricting data collection on the ability to address vital public health questions.

A basic model for assessing primary health care electronic medical record data quality

Background

The increased use of electronic medical records (EMRs) in Canadian primary health care practice has resulted in an expansion of the availability of EMR data. Potential users of these data need to understand their quality in relation to the uses to which they are applied. Herein, we propose a basic model for assessing primary health care EMR data quality, comprising a set of data quality measures within four domains. We describe the process of developing and testing this set of measures, share the results of applying these measures in three EMR-derived datasets, and discuss what this reveals about the measures and EMR data quality. The model is offered as a starting point from which data users can refine their own approach, based on their own needs.

Methods

Using an iterative process, measures of EMR data quality were created within four domains: comparability; completeness; correctness; and currency. We used a series of process steps to develop the measures. The measures were then operationalized, and tested within three datasets created from different EMR software products.

Results

A set of eleven final measures were created. We were not able to calculate results for several measures in one dataset because of the way the data were collected in that specific EMR. Overall, we found variability in the results of testing the measures (e.g. sensitivity values were highest for diabetes, and lowest for obesity), among datasets (e.g. recording of height), and by patient age and sex (e.g. recording of blood pressure, height and weight).

Conclusions

This paper proposes a basic model for assessing primary health care EMR data quality. We developed and tested multiple measures of data quality, within four domains, in three different EMR-derived primary health care datasets. The results of testing these measures indicated that not all measures could be utilized in all datasets, and illustrated variability in data quality. This is one step forward in creating a standard set of measures of data quality. Nonetheless, each project has unique challenges, and therefore requires its own data quality assessment before proceeding.

Electronic supplementary material

The online version of this article (10.1186/s12911-019-0740-0) contains supplementary material, which is available to authorized users.

Simulation of patient flow in multiple healthcare units using process and data mining techniques for model identification

INTRODUCTION

An approach to building a hybrid simulation of patient flow is introduced with a combination of data-driven methods for automation of model identification. The approach is described with a conceptual framework and basic methods for combination of different techniques. The implementation of the proposed approach for simulation of the acute coronary syndrome (ACS) was developed and used in an experimental study.

METHODS

A combination of data, text, process mining techniques, and machine learning approaches for the analysis of electronic health records (EHRs) with discrete-event simulation (DES) and queueing theory for the simulation of patient flow was proposed. The performed analysis of EHRs for ACS patients enabled identification of several classes of clinical pathways (CPs) which were used to implement a more realistic simulation of the patient flow. The developed solution was implemented using Python libraries (SimPy, SciPy, and others).

RESULTS

The proposed approach enables more a realistic and detailed simulation of the patient flow within a group of related departments. An experimental study shows an improved simulation of patient length of stay for ACS patient flow obtained from EHRs in Almazov National Medical Research Centre in Saint Petersburg, Russia.

CONCLUSION

The proposed approach, methods, and solutions provide a conceptual, methodological, and programming framework for the implementation of a simulation of complex and diverse scenarios within a flow of patients for different purposes: decision making, training, management optimization, and others.

Improving the safety of health information technology requires shared responsibility: It is time we all step up

In 2011, an Institute of Medicine report on health information technology (IT) and patient safety highlighted that building health-IT for safer use is a shared responsibility between key stakeholders including: "vendors, care providers, healthcare organizations, health-IT departments, and public and private agencies". Use of electronic health records (EHRs) involves all these stakeholders, but they often have conflicting priorities and requirements. Since 2011, the concept of shared responsibility has gained little traction and EHR developers and users continue to attribute the substantial, long list of problems to each other. In this article, we discuss how these key stakeholders have complementary roles in improving EHR safety and must share responsibility to improve the current state of EHR use. We use real-world safety examples and outline a comprehensive shared responsibility approach to help guide development of future rules, regulations, and standards for EHR usability, interoperability and security as outlined in the 21st Century Cures Act. This approach clearly defines the responsibilities of each party and helps create appropriate measures for success. National and international policymakers must facilitate the local organizational and socio-political climate to stimulate the adoption of shared responsibility principles. When all major stakeholders are sharing responsibility, we will be more likely to usher in a new age of progress and innovation related to health IT.

Visual Acuity Is Correlated with the Area of the Foveal Avascular Zone in Diabetic Retinopathy and Retinal Vein Occlusion

PURPOSE

To determine if the area of the foveal avascular zone (FAZ) is correlated with visual acuity (VA) in diabetic retinopathy (DR) and retinal vein occlusion (RVO).

DESIGN

Cross-sectional study.

PARTICIPANTS

Ninety-five eyes of 66 subjects with DR (65 eyes), branch retinal vein occlusion (19 eyes), and central retinal vein occlusion (11 eyes).

METHODS

Structural optical coherence tomography (OCT; Spectralis, Heidelberg Engineering) and OCT angiography (OCTA; Avanti, Optovue RTVue XR) data from a single visit were analyzed. FAZ area, point thickness of central fovea, central 1-mm subfield thickness, the occurrence of intraretinal cysts, ellipsoid zone disruption, and disorganization of retinal inner layers (DRIL) length were measured. VA was also recorded. Correlations between FAZ area and VA were explored using regression models. Main outcome measure was VA.

RESULTS

Mean age was 62.9±13.2 years. There was no difference in demographic and OCT-derived anatomic measurements between branch retinal vein occlusion and central retinal vein occlusion groups (all P ≥ 0.058); therefore, data from the 2 groups were pooled together to a single RVO group for further statistical comparisons. Univariate and multiple regression analysis showed that the area of the FAZ was significantly correlated with VA in DR and RVO (all P ≤ 0.003). The relationship between FAZ area and VA varied with age (P = 0.026) such that for a constant FAZ area, an increase in patient age was associated with poorer vision (rise in logarithm of the minimum angle of resolution visual acuity). Disruption of the ellipsoid zone was significantly correlated with VA in univariate and multiple regression analysis (both P < 0.001). Occurrence of intraretinal cysts, DRIL length, and lens status were significantly correlated with VA in the univariate regression analysis (P ≤ 0.018) but not the multiple regression analysis (P ≥ 0.210). Remaining variables evaluated in this study were not predictive of VA (all P ≥ 0.225).

CONCLUSIONS

The area of the FAZ is significantly correlated with VA in DR and RVO and this relationship is modulated by patient age. Further study about FAZ area and VA correlations during the natural course of retinal vascular diseases and following treatment is warranted.