scholarly journals Identification of patients at risk of new onset heart failure: Utilizing a large statewide health information exchange to train and validate a risk prediction model

PLoS ONE ◽  
2021 ◽  
Vol 16 (12) ◽  
pp. e0260885
Author(s):  
Son Q. Duong ◽  
Le Zheng ◽  
Minjie Xia ◽  
Bo Jin ◽  
Modi Liu ◽  
...  
Keyword(s):  
Machine Learning ◽  
Heart Failure ◽  
Health Information ◽  
Information Exchange ◽  
Validation Cohort ◽  
Health Information Exchange ◽  
Clinical Care ◽  
Preventative Care ◽  
Increased Risk ◽  
New Onset

Background New-onset heart failure (HF) is associated with poor prognosis and high healthcare utilization. Early identification of patients at increased risk incident-HF may allow for focused allocation of preventative care resources. Health information exchange (HIE) data span the entire spectrum of clinical care, but there are no HIE-based clinical decision support tools for diagnosis of incident-HF. We applied machine-learning methods to model the one-year risk of incident-HF from the Maine statewide-HIE. Methods and results We included subjects aged ≥ 40 years without prior HF ICD9/10 codes during a three-year period from 2015 to 2018, and incident-HF defined as assignment of two outpatient or one inpatient code in a year. A tree-boosting algorithm was used to model the probability of incident-HF in year two from data collected in year one, and then validated in year three. 5,668 of 521,347 patients (1.09%) developed incident-HF in the validation cohort. In the validation cohort, the model c-statistic was 0.824 and at a clinically predetermined risk threshold, 10% of patients identified by the model developed incident-HF and 29% of all incident-HF cases in the state of Maine were identified. Conclusions Utilizing machine learning modeling techniques on passively collected clinical HIE data, we developed and validated an incident-HF prediction tool that performs on par with other models that require proactively collected clinical data. Our algorithm could be integrated into other HIEs to leverage the EMR resources to provide individuals, systems, and payors with a risk stratification tool to allow for targeted resource allocation to reduce incident-HF disease burden on individuals and health care systems.

scholarly journals Balancing health privacy, health information exchange, and research in the context of the COVID-19 pandemic

2020 ◽  
Vol 27 (6) ◽  
pp. 963-966 ◽  
Author(s):  
Leslie Lenert ◽  
Brooke Yeager McSwain
Keyword(s):  
Health Information ◽  
Information Exchange ◽  
Health Information Exchange ◽  
Systems Of Care ◽  
Clinical Care ◽  
The Novel ◽  
Remote Testing ◽  
Specimen Collection ◽  
Care Processes ◽  
Novel Coronavirus

Abstract The novel coronavirus disease 2019 infection poses serious challenges to the healthcare system that are being addressed through the creation of new unique and advanced systems of care with disjointed care processes (eg, telehealth screening, drive-through specimen collection, remote testing, telehealth management). However, our current regulations on the flows of information for clinical care and research are antiquated and often conflict at the state and federal levels. We discuss proposed changes to privacy regulations such as the Health Insurance Portability and Accountability Act designed to let health information seamlessly and frictionlessly flow among the health entities that need to collaborate on treatment of patients and, also, allow it to flow to researchers trying to understand how to limit its impacts.

scholarly journals Beyond Interoperability with The Single Patient Viewer: A Clinical Portal to Access Integrated Patient Records

2020 ◽  
Vol 5 (5) ◽  
Author(s):  
Rosemary Foster ◽  
Alexa Heekes ◽  
Hannah Hussey ◽  
Mariette Smith ◽  
Themba Mutemaringa ◽  
...  
Keyword(s):  
Health Information ◽  
Information Exchange ◽  
Health Information Exchange ◽  
Clinical Care ◽  
Data Access ◽  
Global Public Good ◽  
Single Patient ◽  
Patient Level Data ◽  
Patient Level ◽  
Level Data

IntroductionIn the Western Cape Province of South Africa, a wealth of patient-level data is collected through many separate electronic systems, which share a unique health identifier. However, clinicians primarily access paper folders, which can be unreliable, difficult to locate and are at risk of loss. Patients frequently attend multiple facilities and their information may not be accessible across facilities, hampering continuity of care. Objectives and ApproachFacilitated by the unique health identifier, a provincial Health Information Exchange (HIE), harmonises patient level data from routine systems into a health information exchange daily. The Single Patient Viewer (SPV) has been developed as a prototype web-based electronic health record and data access portal. SPV integrates clinical data for a single patient both longitudinally and cross-sectionally, in tabular and graphical views, to assist clinicians in rapid information discovery. The application is designed as a web application that calls a multi-purpose API that facilitates interoperability with the HIE. ResultSPV is in an extended pilot phase with over 200 clinicians using the portal, either for clinical care provision, or for clinical audit activities. The application has evolved to include referral, follow-up (voice call and messaging) and reporting functionality. In the past 6 months, over 17,000 patients have been viewed with an average daily search of 150 patients. An anonymised user survey with 52 participants showed that users felt that SPV was enjoyable to use, easy to learn, innovative, and supportive and valuable to their work. Conclusion / ImplicationsSPV has been developed as a global public good project and will be made freely available once matured. A unique feature of the development of SPV is that clinicians were using it while it was being built, enabling constant clinical user feedback. SPV demonstrates the value of integrating health data for clinical viewing while interoperable systems mature.

New-onset type 2 diabetes in heart failure: impact of heart failure and death versus ischemic events – a Danish nationwide cohort study

2020 ◽  
Vol 41 (Supplement_2) ◽  
Author(s):  
B Zareini ◽  
P.B Blanche ◽  
A.H Holt ◽  
M.M Malik ◽  
D.P Rajan ◽  
...  
Keyword(s):  
Heart Failure ◽  
Type 2 Diabetes ◽  
Risk Ratio ◽  
Real Life ◽  
Ischemic Event ◽  
Increased Risk ◽  
The Absolute ◽  
Ischemic Events ◽  
New Onset

Abstract Background Development of type 2 diabetes (T2D) is common in patients with heart failure (HF), but knowledge of future cardiovascular events is lacking. Purpose We compared risk of heart failure hospitalization (HFH) or death versus ischemic events in real-life HF patients with new-onset T2D, prevalent T2D and no T2D. Methods Using the Danish nationwide registers, we identified all patients with HF between 1998–2016. The patients were separated in two different HF cohorts based on the status of T2D. One cohort consisted of HF patients with either prevalent or absent T2D at the time of HF diagnosis. The other cohort consisted of HF patients, who developed new-onset T2D, included at time of diagnosis. The two HF cohorts were analyzed separately. Outcomes for both cohorts were analyzed as time-to-first event as either an ischemic event (i.e. composite outcome of fatal and non-fatal myocardial infarction, stroke, and peripheral artery disease), HFH, or event-free death (not related to HFH or the ischemic event). For each cohort, we estimated the five-year absolute risk of ischemic event, HFH and event-free death, along with five-year risk ratio of HFH or event-free death versus ischemic events. Effects among subgroups were investigated by stratifying both cohorts based on age, gender and comorbidities present at inclusion. Results A total of 139,264 HF patients were included between 1998 and 2016, of which 29,078 (21%) patients had prevalent T2D at baseline. A total of 11,819 (8%) developed new-onset T2D and were included in the second cohort. The median duration of time between HF diagnosis and new-onset T2D diagnosis was: 4.1 years (IQR:1.5; 5.8). The absolute five-year risk of an ischemic event in patients with new-onset T2D, prevalent T2D and no T2D was: 17.9% (95% confidence interval (CI): 17.2; 18.6), 26.1% (95% CI: 25.6; 26.7), and 18.8% (95% CI:18.6; 19.0). Corresponding estimates for HFH were: 31.5% (95% CI: 30.6; 32.3), 33.6% (95% CI: 33.0; 34.2), and 30,7% (95% CI: 30.5; 31.0). The absolute five-year risk of event-free death among patients with new-onset T2D, prevalent T2D and no T2D was: 20.9% (95% CI: 20.2; 21.7), 18.9% (95% CI:18.4; 19.3), and 18.6% (95% CI: 18.4; 18.8) (see Figure). The five-year risk ratio of experiencing HFH or event-free death versus an ischemic event was: 2.9 (95% CI: 2.8; 3.1), 2.0 (95% CI:2.0; 2.1), and 2.6 (95% CI: 2.6; 2.7) for patients with new-onset T2D, prevalent T2D and no T2D, respectively. Similar results of absolute and relative risk were present across all subgroups. Conclusion In our population of HF patients, 8% developed new-onset diabetes. Development of T2D in patients with HF increases the risk of HFH and mortality three-fold. The increased risk of new-onset T2D is higher than the importance of prevalent T2D in patients with HF. Funding Acknowledgement Type of funding source: None

scholarly journals Diabetes and COVID19: a bidirectional relationship

2021 ◽  
Author(s):  
Ranjit Unnikrishnan ◽  
Anoop Misra
Keyword(s):  
Clinical Care ◽  
Severe Disease ◽  
Respiratory Involvement ◽  
Pancreatic Damage ◽  
Increased Risk ◽  
Bidirectional Relationship ◽  
Rapid Spread ◽  
Relationship Of ◽  
The Relationship ◽  
New Onset

AbstractThe advent and rapid spread of the coronavirus disease-2019 (COVID19) pandemic across the world has focused attention on the relationship of commonly occurring comorbidities such as diabetes on the course and outcomes of this infection. While diabetes does not seem to be associated with an increased risk of COVID19 infection per se, it has been clearly demonstrated that the presence of hyperglycemia of any degree predisposes to worse outcomes, such as more severe respiratory involvement, ICU admissions, need for mechanical ventilation and mortality. Further, COVID19 infection has been associated with the development of new-onset hyperglycemia and diabetes, and worsening of glycemic control in pre-existing diabetes, due to direct pancreatic damage by the virus, body’s stress response to infection (including cytokine storm) and use of diabetogenic drugs such as corticosteroids in the treatment of severe COVID19. In addition, public health measures taken to flatten the pandemic curve (such as lockdowns) can also adversely impact persons with diabetes by limiting their access to clinical care, healthy diet, and opportunities to exercise. Most antidiabetic medications can continue to be used in patients with mild COVID19 but switching over to insulin is preferred in severe disease.

scholarly journals Diabetes and COVID19: a bidirectional relationship

2021 ◽  
Vol 11 (1) ◽  
Author(s):  
Ranjit Unnikrishnan ◽  
Anoop Misra
Keyword(s):  
Clinical Care ◽  
Severe Disease ◽  
Respiratory Involvement ◽  
Pancreatic Damage ◽  
Increased Risk ◽  
Bidirectional Relationship ◽  
Rapid Spread ◽  
Relationship Of ◽  
The Relationship ◽  
New Onset

AbstractThe advent and rapid spread of the coronavirus disease-2019 (COVID19) pandemic across the world has focused attention on the relationship of commonly occurring comorbidities such as diabetes on the course and outcomes of this infection. While diabetes does not seem to be associated with an increased risk of COVID19 infection per se, it has been clearly demonstrated that the presence of hyperglycemia of any degree predisposes to worse outcomes, such as more severe respiratory involvement, ICU admissions, need for mechanical ventilation and mortality. Further, COVID19 infection has been associated with the development of new-onset hyperglycemia and diabetes, and worsening of glycemic control in pre-existing diabetes, due to direct pancreatic damage by the virus, body’s stress response to infection (including cytokine storm) and use of diabetogenic drugs such as corticosteroids in the treatment of severe COVID19. In addition, public health measures taken to flatten the pandemic curve (such as lockdowns) can also adversely impact persons with diabetes by limiting their access to clinical care, healthy diet, and opportunities to exercise. Most antidiabetic medications can continue to be used in patients with mild COVID19 but switching over to insulin is preferred in severe disease.

scholarly journals Maturity assessment of Kenya’s health information system interoperability readiness

2021 ◽  
Vol 28 (1) ◽  
pp. e100241
Author(s):  
Job Nyangena ◽  
Rohini Rajgopal ◽  
Elizabeth Adhiambo Ombech ◽  
Enock Oloo ◽  
Humphrey Luchetu ◽  
...  
Keyword(s):  
Health Information ◽  
Information Exchange ◽  
Working Group ◽  
Data Exchange ◽  
Enterprise Architecture ◽  
Health Information Exchange ◽  
Digital Health ◽  
Health Information System ◽  
Maturity Level ◽  
Technical Standards

BackgroundThe use of digital technology in healthcare promises to improve quality of care and reduce costs over time. This promise will be difficult to attain without interoperability: facilitating seamless health information exchange between the deployed digital health information systems (HIS).ObjectiveTo determine the maturity readiness of the interoperability capacity of Kenya’s HIS.MethodsWe used the HIS Interoperability Maturity Toolkit, developed by MEASURE Evaluation and the Health Data Collaborative’s Digital Health and Interoperability Working Group. The assessment was undertaken by eHealth stakeholder representatives primarily from the Ministry of Health’s Digital Health Technical Working Group. The toolkit focused on three major domains: leadership and governance, human resources and technology.ResultsMost domains are at the lowest two levels of maturity: nascent or emerging. At the nascent level, HIS activities happen by chance or represent isolated, ad hoc efforts. An emerging maturity level characterises a system with defined HIS processes and structures. However, such processes are not systematically documented and lack ongoing monitoring mechanisms.ConclusionNone of the domains had a maturity level greater than level 2 (emerging). The subdomains of governance structures for HIS, defined national enterprise architecture for HIS, defined technical standards for data exchange, nationwide communication network infrastructure, and capacity for operations and maintenance of hardware attained higher maturity levels. These findings are similar to those from interoperability maturity assessments done in Ghana and Uganda.

scholarly journals Despite Substantial Progress In EHR Adoption, Health Information Exchange And Patient Engagement Remain Low In Office Settings

2014 ◽  
Vol 33 (9) ◽  
pp. 1672-1679 ◽  
Author(s):  
Michael F. Furukawa ◽  
Jennifer King ◽  
Vaishali Patel ◽  
Chun-Ju Hsiao ◽  
Julia Adler-Milstein ◽  
...  
Keyword(s):  
Health Information ◽  
Patient Engagement ◽  
Information Exchange ◽  
Health Information Exchange ◽  
Substantial Progress ◽  
Ehr Adoption

Abstract 18599: Clinical Assessment of Hemodynamic Profiles Predict Readmission and Mortality in Diastolic Heart Failure Patients

Circulation ◽  
2015 ◽  
Vol 132 (suppl_3) ◽  
Author(s):  
Mohammed Siddiqui ◽  
Salpy V Pamboukian ◽  
Jose A Tallaj ◽  
Michael Falola ◽  
Sula Mazimba
Keyword(s):  
Heart Failure ◽  
Clinical Assessment ◽  
Diastolic Heart Failure ◽  
Clinical Care ◽  
Systolic Heart Failure ◽  
Health Care Expenditures ◽  
Left Ventricular ◽  
Readmission Rates ◽  
Heart Failure Patients ◽  
Increased Risk

Background: Reducing 30 day readmission rates for patients with heart failure (HF) has been a recent focus of lowering health care expenditures. Hemodynamic profiles (HP) have been associated with clinical outcomes in chronic systolic HF. The relationship of HP to outcomes in acute decompensated diastolic HF (DHF) has not been defined. Methods: This case-control study of 1892 DHF patients discharged alive from an academic hospital between 2002-2012 with left ventricular function greater or equal to 45% were categorized into 4 groups: Profile A, no evidence of congestion and hypoperfusion (dry-warm); Profile B, congestion with adequate perfusion (wet-warm); Profile C, congestion with hypoperfusion (wet-cold); and Profile L, hypoperfusion without congestion (dry-cold). All cause readmissions at 30 days and 1 year and mortality at 30 days and 1 year were examined. Statistical analysis using multivariable Cox Proportional hazard model was performed adjusting for demographic, clinical, care and hospital characteristics. Results: Of the 1892 patients, 1196 (63%) were females; mean age was 68 (±14) years. There were 724(38%), 1000 (53%), 88(5%) and 80 (4%) patients in the hemodynamic profiles A, B, C and L respectively. Profiles B and C were associated with an increased risk for 30-day all-cause HF readmission compared to profiles A and L: Hazard ratio (HR) [1.38 (95% C.I 1.17-1.61)], [1.39 (95% C.I 1.18-1.62)] for B and C profiles respectively. Profiles C and L were associated with increased mortality at 1 year: HR [1.46 (95% CI 1.06-1.89)] and [1.31 (95% CI 1.01-1.64)] for A and L profiles respectively (Table). Conclusions: Clinical assessment of HP can help identify DHF patients at increased risk of readmission and mortality, similar to systolic heart failure patients.

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