Finding Potential Adverse Events in the Unstructured Text of Electronic Health Care Records: Development of the Shakespeare Method

Background Big data tools provide opportunities to monitor adverse events (patient harm associated with medical care) (AEs) in the unstructured text of electronic health care records (EHRs). Writers may explicitly state an apparent association between treatment and adverse outcome (“attributed”) or state the simple treatment and outcome without an association (“unattributed”). Many methods for finding AEs in text rely on predefining possible AEs before searching for prespecified words and phrases or manual labeling (standardization) by investigators. We developed a method to identify possible AEs, even if unknown or unattributed, without any prespecifications or standardization of notes. Our method was inspired by word-frequency analysis methods used to uncover the true authorship of disputed works credited to William Shakespeare. We chose two use cases, “transfusion” and “time-based.” Transfusion was chosen because new transfusion AE types were becoming recognized during the study data period; therefore, we anticipated an opportunity to find unattributed potential AEs (PAEs) in the notes. With the time-based case, we wanted to simulate near real-time surveillance. We chose time periods in the hope of detecting PAEs due to contaminated heparin from mid-2007 to mid-2008 that were announced in early 2008. We hypothesized that the prevalence of contaminated heparin may have been widespread enough to manifest in EHRs through symptoms related to heparin AEs, independent of clinicians’ documentation of attributed AEs. Objective We aimed to develop a new method to identify attributed and unattributed PAEs using the unstructured text of EHRs. Methods We used EHRs for adult critical care admissions at a major teaching hospital (2001-2012). For each case, we formed a group of interest and a comparison group. We concatenated the text notes for each admission into one document sorted by date, and deleted replicate sentences and lists. We identified statistically significant words in the group of interest versus the comparison group. Documents in the group of interest were filtered to those words, followed by topic modeling on the filtered documents to produce topics. For each topic, the three documents with the maximum topic scores were manually reviewed to identify PAEs. Results Topics centered around medical conditions that were unique to or more common in the group of interest, including PAEs. In each use case, most PAEs were unattributed in the notes. Among the transfusion PAEs was unattributed evidence of transfusion-associated cardiac overload and transfusion-related acute lung injury. Some of the PAEs from mid-2007 to mid-2008 were increased unattributed events consistent with AEs related to heparin contamination. Conclusions The Shakespeare method could be a useful supplement to AE reporting and surveillance of structured EHR data. Future improvements should include automation of the manual review process.

Evaluation of Potential Drug-Drug Interactions among Patients of the Nephrology and Kidney Transplant Wards of a Major Teaching Hospital in Iran

Aims: This study was aimed to find the prevalence of potential DDIs in patients and identify factors associated with these interactions. Study design: All patients' medication regimens were screened for potential DDIs through Lexi-Interact® Online application. Place and Duration of Study: This study was conducted for five months in 2017-2018 at the nephrology and kidney transplant ward of Razi hospital, Rasht, Iran. Methodology: Each potential DDI was characterized based on severity, onset, mechanism, risk rating and reliability rating. The patient's comorbidity was assessed with the Charlson comorbidity index. The quality of patients' life was assessed with the Kidney Disease Quality of Life Instrument-SF36TM questionnaire. Results: The study included 191 patients (109 [57.07%] males and 82 [42.93%] females) with a mean age of 58.09 ± 17.76 years. The analysis revealed that 29.4 % of potential DDIs had good and 13.5% had excellent evidence. There was a statistically significant association among the number of prescribed medications (polypharmacy), hospital ward, age, Body Mass Index, education, history of drug addiction, length of hospitalization, dyslipidemia, and hypothyroidism. Conclusion Potential DDIs are common in patients of the nephrology and kidney transplant wards, so proper patient monitoring is essential for minimizing and preventing potential adverse outcomes of DDIs.

Evaluation of Albumin Utilization in a Major Teaching Hospital in Iran Before and After Guideline Implementation

Background: Inappropriate use of drugs is one of the major issues in health care system. Rational drug utilization based on the appropriate guidelines has an important role in management of use of expensive medications. We aimed to evaluate albumin usage's appropriateness based on evidence-based indications before and after implementing albumin prescription guideline in a teaching hospital. Methods: This study was performed in two phases. During two-month periods, all the patients who were ordered to receive albumin were evaluated. The first phase was done in November and December of 2017, during which, based on physicians' comments, the guideline was finalized and then implemented. Phase two was performed in May and June 2018. Results: Albumin was prescribed appropriately in 33 patients (55%) in the first phase and 43 (70%) patients in the second phase. 299 vials in the first phase and 456 vials in the second phase were prescribed which 198 vials (66%) and 394 (86%) vials were used with appropriate indications, respectively. The number of vials consumed with inappropriate indication decreased significantly from 101 vials (34%) in the first phase to 62 vials (14%) in the second phase (P-value=0.01). The average cost of the inappropriate indication per patient decreased from $197.3 ± 131.6 in the first phase to $183.5 ± 126.8 in the second phase (P-value=0.52). Conclusion: This study showed implementing a DUE program and designing a guideline for rational prescribing of albumin and interventional methods can optimize treatment duration, significantly decrease inappropriate usage, and avoid unnecessary hospital costs.

Application of verbal autopsy in routine civil registration in Lusaka District of Zambia

Background Ascertaining the causes for deaths occurring outside health facilities is a significant problem in many developing countries where civil registration systems are not well developed or non-functional. Standardized and rigorous verbal autopsy methods is a potential solution to determine the cause of death. We conducted a demonstration project in Lusaka District of Zambia where verbal autopsy (VA) method was implemented in routine civil registration system. Methods About 3400 VA interviews were conducted for bodies “brought-in-dead” at Lusaka’s two major teaching hospital mortuaries using a SmartVA questionnaire between October 2017 and September 2018. Probable underlying causes of deaths using VA and cause-specific mortality fractions were determined.. Demographic characteristics were analyzed for each VA-ascertained cause of death. Results Opportunistic infections (OIs) associated with HIV/AIDS such as pneumonia and tuberculosis, and malaria were among leading causes of deaths among bodies “brought-in-dead”. Over 21.6 and 26.9% of deaths were attributable to external causes and non-communicable diseases (NCDs), respectively. The VA-ascertained causes of death varied by age-group and sex. External causes were more prevalent among males in middle ages (put an age range like 30–54 years old) and NCDs highly prevalent among those aged 55 years and older. Conclusions VA application in civil registration system can provide the much-needed cause of death information for non-facility deaths in countries with under-developed or non-functional civil registration systems.

Changing practice for neuraxial applications using NRFit™ small-bore connectors to improve patient safety

Clinical staff always aim to offer the best care for their patients while striving to minimise the risk of errors. The worldwide adoption of the NRFit™ system for neuraxial and regional block procedures is a major step forward. This article discusses the history of neuraxial needles and the experience of a major teaching hospital in adopting non-luer equipment for neuraxial procedures. References are made to resources that are available for other hospitals in the process of implementing the change to the NRFit system, which should result in the reduction of harm to patients.

Living with COVID-19: a phenomenological study of hospitalised patients involved in family cluster transmission

ObjectivesTo describe experiences of hospitalised patients with COVID-19 following family cluster transmission of the infection and the meaning of these experiences for them.DesignA descriptive phenomenological design was used to construct themes depicting patients’ experiences of living with COVID-19.SettingThis study was conducted in a major teaching hospital in Wuhan, China, in March 2020.ParticipantsFourteen patients involved in family cluster transmission of COVID-19 were recruited into the study. The participants consisted of seven males and seven females. Data were collected through semistructured, in-depth, face-to-face interviews. Interviews were transcribed verbatim and analysed using Colaizzi’s approach.ResultsSix themes emerged from data analysis during two distinct phases of patients going through COVID-19: the early outbreak phase and the later hospitalisation phase. Early in the outbreak, patients experienced life imbalances between individual well-being and family responsibilities. While facing widespread prejudice and rejection, patients dealt with the heavy toll that the illness had left on their body and mind. After being hospitalised, patients described feelings of living with uncertainty, sadness, fear of death and concerns about family, while simultaneously hoping for a better life after recovery.ConclusionsOur findings suggest that living with COVID-19 is an emotionally and physically challenging experience for patient participants in the study. Psychological evaluations need to be routinely carried out with patients in a public health crisis. Interprofessional and interorganisational collaborative efforts should be made to examine the physical and psychological sequelae of COVID-19, as well as investigate outcomes of existing intervention programmes.

Finding Potential Adverse Events in the Unstructured Text of Electronic Health Care Records: Development of the Shakespeare Method (Preprint)

BACKGROUND Big data tools provide opportunities to monitor adverse events (patient harm associated with medical care) (AEs) in the unstructured text of electronic health care records (EHRs). Writers may explicitly state an apparent association between treatment and adverse outcome (“attributed”) or state the simple treatment and outcome without an association (“unattributed”). Many methods for finding AEs in text rely on predefining possible AEs before searching for prespecified words and phrases or manual labeling (standardization) by investigators. We developed a method to identify possible AEs, even if unknown or unattributed, without any prespecifications or standardization of notes. Our method was inspired by word-frequency analysis methods used to uncover the true authorship of disputed works credited to William Shakespeare. We chose two use cases, “transfusion” and “time-based.” Transfusion was chosen because new transfusion AE types were becoming recognized during the study data period; therefore, we anticipated an opportunity to find unattributed potential AEs (PAEs) in the notes. With the time-based case, we wanted to simulate near real-time surveillance. We chose time periods in the hope of detecting PAEs due to contaminated heparin from mid-2007 to mid-2008 that were announced in early 2008. We hypothesized that the prevalence of contaminated heparin may have been widespread enough to manifest in EHRs through symptoms related to heparin AEs, independent of clinicians’ documentation of attributed AEs. OBJECTIVE We aimed to develop a new method to identify attributed and unattributed PAEs using the unstructured text of EHRs. METHODS We used EHRs for adult critical care admissions at a major teaching hospital (2001-2012). For each case, we formed a group of interest and a comparison group. We concatenated the text notes for each admission into one document sorted by date, and deleted replicate sentences and lists. We identified statistically significant words in the group of interest versus the comparison group. Documents in the group of interest were filtered to those words, followed by topic modeling on the filtered documents to produce topics. For each topic, the three documents with the maximum topic scores were manually reviewed to identify PAEs. RESULTS Topics centered around medical conditions that were unique to or more common in the group of interest, including PAEs. In each use case, most PAEs were unattributed in the notes. Among the transfusion PAEs was unattributed evidence of transfusion-associated cardiac overload and transfusion-related acute lung injury. Some of the PAEs from mid-2007 to mid-2008 were increased unattributed events consistent with AEs related to heparin contamination. CONCLUSIONS The Shakespeare method could be a useful supplement to AE reporting and surveillance of structured EHR data. Future improvements should include automation of the manual review process.

Use of Diagnosis Codes to Find Blood Transfusion Adverse Events in Electronic Health Records

ABSTRACTBackgroundElectronic health records (EHRs) and big data tools offer the opportunity for surveillance of adverse events (patient harm associated with medical care). We chose the case of transfusion adverse events (TAEs) and potential TAEs (PTAEs) because 1.) real dates were obscured in the study data, and 2.) there was emerging recognition of new types during the study data period.ObjectiveWe aimed to use the structured data in electronic health records (EHRs) to find TAEs and PTAEs among adults.MethodsWe used 49,331 adult admissions involving critical care at a major teaching hospital, 2001-2012, in the MIMIC-III EHRs database. We formed a T (defined as packed red blood cells, platelets, or plasma) group of 21,443 admissions vs. 25,468 comparison (C) admissions. The ICD-9-CM diagnosis codes were compared for T vs. C, described, and tested with statistical tools.ResultsTAEs such as transfusion associated circulatory overload (TACO; 12 T cases; rate ratio (RR) 15.61; 95% CI 2.49 to 98) were found. There were also PTAEs similar to TAEs, such as fluid overload disorder (361 T admissions; RR 2.24; 95% CI 1.88 to 2.65), similar to TACO. Some diagnoses could have been sequelae of TAEs, including nontraumatic compartment syndrome of abdomen (52 T cases; RR 6.76; 95% CI 3.40 to 14.9) possibly being a consequence of TACO.ConclusionsSurveillance for diagnosis codes that could be TAE sequelae or unrecognized TAE might be useful supplements to existing medical product adverse event programs.

COVID-19 Antibodies and Outcomes Among Outpatient Maintenance Hemodialysis Patients

Background: Maintenance hemodialysis patients are particularly vulnerable to infection and hospitalization with the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) virus. Due to immunocompromise and clustering in outpatient dialysis units, the seroprevalence of COVID-19 antibodies in this population is unknown and has significant implications for public health. Little is also known about their risk factors for hospitalization. Methods: Three outpatient maintenance hemodialysis units affiliated with a major teaching hospital in the New York area were studied. We determined rates of SARS-CoV-2 nasopharyngeal real-time reverse transcriptase polymerase chain reaction (RT-PCR) positivity, SARS-CoV-2 IgG seropositivity, hospitalization, and mortality. Results: Of 367 patients, 28.3% had either SARS-CoV-2 seropositivity or PCR positivity. Prevalence across the three units was 6.7%, 32.3%, and 69.6%. Those who were either antibody or PCR positive were significantly younger (65 vs 69 years, p=0.046), and had higher prevalence of black race (43.3% vs 29.7%, p = 0.001) and Hispanic ethnicity (31.7% vs 11.8%, p < 0.001) compared to those who tested negative. Higher positivity rates were also observed among those who took taxis and ambulettes to and from dialysis, relative to those who used personal transportation. Antibodies were detected in all PCR positive patients testing who underwent serologic testing. Of those that were seropositive, 31.8% were asymptomatic. The hospitalization rate based on either antibody or PCR positivity was 34.6%, with a hospital mortality rate of 33.3%. Aside from COPD, no other variables were more prevalent in hospitalized patients. Conclusions: We observed significant differences in rates of COVID-19 infection within three outpatient dialysis units, with universal seroconversion. Among patients with ESRD, rates of asymptomatic infection appear to be high, as do hospitalization and mortality rates.