Pub Date : 2020-07-28DOI: 10.1101/2020.07.25.20162131
Xiang Gao, Q. Dong
Estimating the hospitalization risk for people with certain comorbidities infected by the SARS-CoV-2 virus is important for developing public health policies and guidance based on risk stratification. Traditional biostatistical methods require knowing both the number of infected people who were hospitalized and the number of infected people who were not hospitalized. However, the latter may be undercounted, as it is limited to only those who were tested for viral infection. In addition, comorbidity information for people not hospitalized may not always be readily available for traditional biostatistical analyses. To overcome these limitations, we developed a Bayesian approach that only requires the observed frequency of comorbidities in COVID-19 patients in hospitals and the prevalence of comorbidities in the general population. By applying our approach to two different large-scale datasets in the U.S., our results consistently indicated that cardiovascular diseases carried the highest hospitalization risk for COVID-19 patients, followed by diabetes, chronic respiratory disease, hypertension, and obesity, respectively.
{"title":"A Bayesian Framework for Estimating the Risk Ratio of Hospitalization for People with Comorbidity Infected by the SARS-CoV-2 Virus","authors":"Xiang Gao, Q. Dong","doi":"10.1101/2020.07.25.20162131","DOIUrl":"https://doi.org/10.1101/2020.07.25.20162131","url":null,"abstract":"Estimating the hospitalization risk for people with certain comorbidities infected by the SARS-CoV-2 virus is important for developing public health policies and guidance based on risk stratification. Traditional biostatistical methods require knowing both the number of infected people who were hospitalized and the number of infected people who were not hospitalized. However, the latter may be undercounted, as it is limited to only those who were tested for viral infection. In addition, comorbidity information for people not hospitalized may not always be readily available for traditional biostatistical analyses. To overcome these limitations, we developed a Bayesian approach that only requires the observed frequency of comorbidities in COVID-19 patients in hospitals and the prevalence of comorbidities in the general population. By applying our approach to two different large-scale datasets in the U.S., our results consistently indicated that cardiovascular diseases carried the highest hospitalization risk for COVID-19 patients, followed by diabetes, chronic respiratory disease, hypertension, and obesity, respectively.","PeriodicalId":344533,"journal":{"name":"J. Am. Medical Informatics Assoc.","volume":"67 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-07-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"132002114","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
{"title":"Breadth and Diversity in Biomedical and Health Informatics","authors":"S. Bakken","doi":"10.1093/JAMIA/OCZ055","DOIUrl":"https://doi.org/10.1093/JAMIA/OCZ055","url":null,"abstract":"","PeriodicalId":344533,"journal":{"name":"J. Am. Medical Informatics Assoc.","volume":"24 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"123969379","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
It is hard to believe that, just a decade ago, the technology of smart phones and the patient-centered healthcare movement were just starting to take off, and many of their current instantiations were not spelled out in visions of a connected healthcare system. Accordingly, informatics has evolved and expanded so fast in the past ten years that it is not a surprise that the volume, quality, and interest in scholarly articles describing the many facets of including providers and patients, as well as patients’ family and friends, in healthcare has increased so much that we are able to dedicate a full issue of JAMIA to research and applications in these areas. Kishore (p. 931) describes a model of the effects of online informational and emotional support on self-care behavior of HIV patients, Cheung (p. 955) evaluates a recommender app for measuring longitudinal user engagement in apps for depression and anxiety treatment, and Utrankar (p. 976) reports on technology use and preferences in supporting clinical practice guideline awareness and adherence in individuals with sickle cell disease. Additionally, Taylor (p. 989) describes the role of family and friends in helping older adults manage personal health information, while Sharko (p. 1008) describes the unique privacy needs of adolescent patients and the resulting complexity of the decision-making process. Different methods have been borrowed from various disciplines over time to fill the needs of provider-, patientand other caregiverfacing applications. Bautista (p. 1018) reports on a psychometric evaluation of a scale to measure nurses’ use of smartphones for work purposes, Reese (p. 1026) uses card sorting methods to elicit expert knowledge in an ICU setting, and Pandolfe (p. 1047) proposes an architecture for a medication reconciliation application that aims at increasing patient activation and education. While the intent of information systems is always to improve care and promote health, positive and negative consequences have been reported in the literature. In this issue of JAMIA, Nouri (p. 1089) systematically reviews criteria for assessing the quality of mHealth apps, Veinot (p. 1080) discusses how informatics interventions can worsen inequality, Meyerhoefer (p. 1054) reports on provider and patient satisfaction with the integration of ambulatory and hospital EHR systems, and Plante (p. 1074) reveals trends in user ratings and reviews of a blood pressure-measuring smartphone app. Increased data sharing of clinical data, partly due to the popularity of patient-facing applications and a realization that faster biomedical discoveries may happen with the use of “big data,” also brings important issues related to ethics and how information is relayed to users. Stahl (p. 1102) discusses the role of ethics in data governance of a large neuro-ICT project, Tao (p. 1036) discusses the effects of graphical formats of self-monitoring test results for consumers, Karpefors (p. 1069) proposes a visual
{"title":"Informatics for all: from provider- to patient-based applications that can include family and friends","authors":"L. Ohno-Machado","doi":"10.1093/jamia/ocy078","DOIUrl":"https://doi.org/10.1093/jamia/ocy078","url":null,"abstract":"It is hard to believe that, just a decade ago, the technology of smart phones and the patient-centered healthcare movement were just starting to take off, and many of their current instantiations were not spelled out in visions of a connected healthcare system. Accordingly, informatics has evolved and expanded so fast in the past ten years that it is not a surprise that the volume, quality, and interest in scholarly articles describing the many facets of including providers and patients, as well as patients’ family and friends, in healthcare has increased so much that we are able to dedicate a full issue of JAMIA to research and applications in these areas. Kishore (p. 931) describes a model of the effects of online informational and emotional support on self-care behavior of HIV patients, Cheung (p. 955) evaluates a recommender app for measuring longitudinal user engagement in apps for depression and anxiety treatment, and Utrankar (p. 976) reports on technology use and preferences in supporting clinical practice guideline awareness and adherence in individuals with sickle cell disease. Additionally, Taylor (p. 989) describes the role of family and friends in helping older adults manage personal health information, while Sharko (p. 1008) describes the unique privacy needs of adolescent patients and the resulting complexity of the decision-making process. Different methods have been borrowed from various disciplines over time to fill the needs of provider-, patientand other caregiverfacing applications. Bautista (p. 1018) reports on a psychometric evaluation of a scale to measure nurses’ use of smartphones for work purposes, Reese (p. 1026) uses card sorting methods to elicit expert knowledge in an ICU setting, and Pandolfe (p. 1047) proposes an architecture for a medication reconciliation application that aims at increasing patient activation and education. While the intent of information systems is always to improve care and promote health, positive and negative consequences have been reported in the literature. In this issue of JAMIA, Nouri (p. 1089) systematically reviews criteria for assessing the quality of mHealth apps, Veinot (p. 1080) discusses how informatics interventions can worsen inequality, Meyerhoefer (p. 1054) reports on provider and patient satisfaction with the integration of ambulatory and hospital EHR systems, and Plante (p. 1074) reveals trends in user ratings and reviews of a blood pressure-measuring smartphone app. Increased data sharing of clinical data, partly due to the popularity of patient-facing applications and a realization that faster biomedical discoveries may happen with the use of “big data,” also brings important issues related to ethics and how information is relayed to users. Stahl (p. 1102) discusses the role of ethics in data governance of a large neuro-ICT project, Tao (p. 1036) discusses the effects of graphical formats of self-monitoring test results for consumers, Karpefors (p. 1069) proposes a visual ","PeriodicalId":344533,"journal":{"name":"J. Am. Medical Informatics Assoc.","volume":"56 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2018-07-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"121455968","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
This issue of JAMIA is focused on informatics applications to enhance patient safety. This is one of the most important, yet underemphasized, aspects of the informatics curriculum across the country. Although media attention occasionally concentrates on what can go wrong when information systems are employed in practice, there is also much to say on what might go wrong if information systems were not employed. Additionally, a proper amount of standardization of clinical practices can elevate sub-optimal care to an acceptable level, often reducing cost and patient suffering as a result. Decision support for medication prescribing and dispensing has always been one of the most direct ways for information systems to promote patient safety. Whalen (p. 849) reports on lessons learned in a pediatrics hospital from a transition to a new electronic health record (EHR) system, and Walsh (p. 911) studies the accuracy of the medication list in the EHR and its implications for care, research, and improvement. Cheng (p. 873) shows how using drug knowledgebase information to distinguish between commonly confused drugs can prevent errors, and Vajravelu (p. 780) proposes a new algorithm to analyze multiple pharmacologic exposures using EHR data. Additionally, Samwald (p. 895) shares the experience of implementing pharmacogenomics decision support across seven European countries. The domains in which information systems can improve patient safety are numerous. Waters (p. 901) studies current use, interest, and perceived usability of clinical pathways for primary care, while Sittig (p. 915) describes the levels of adherence to recommended EHR safety practices across eight healthcare organizations. EHRbased intervention and reports on several safety topics are also presented in this issue of JAMIA: Ray (p. 863) uses statistical anomaly detection models for decision support system malfunctions, Chen (p. 790) analyzes interaction patterns of trauma providers that are associated with increased patients’ lengths of stay in the hospital, while Vahdat (p. 827) reports on a simulation study of the effects of EHR implementation on timeliness of care in a dermatology clinic. Berger (p. 833) integrates physical abuse measures into a pediatric clinical decision support system, while Meyer (p. 841) evaluates a mobile application to improve clinical laboratory test ordering. The applications and algorithms described in this issue of JAMIA would be hard to implement without standardization of terminologies, ontologies, and foundational research in natural language processing and information retrieval. Examples of advances in these areas are also featured: Cuzzola (p. 819) links UMLS to DBpedia to promote knowledge discovery, Wang (p. 809) describes efforts involving RxNorm that are leading to a normalized clinical drug knowledge base in China, Vreeman (p. 886) presents a unified terminology for radiology procedures (the “LOINC RSNA Radiology Playbook”), and Blosnich (p. 907) sho
{"title":"The role of informatics in promoting patient safety","authors":"L. Ohno-Machado","doi":"10.1093/jamia/ocy079","DOIUrl":"https://doi.org/10.1093/jamia/ocy079","url":null,"abstract":"This issue of JAMIA is focused on informatics applications to enhance patient safety. This is one of the most important, yet underemphasized, aspects of the informatics curriculum across the country. Although media attention occasionally concentrates on what can go wrong when information systems are employed in practice, there is also much to say on what might go wrong if information systems were not employed. Additionally, a proper amount of standardization of clinical practices can elevate sub-optimal care to an acceptable level, often reducing cost and patient suffering as a result. Decision support for medication prescribing and dispensing has always been one of the most direct ways for information systems to promote patient safety. Whalen (p. 849) reports on lessons learned in a pediatrics hospital from a transition to a new electronic health record (EHR) system, and Walsh (p. 911) studies the accuracy of the medication list in the EHR and its implications for care, research, and improvement. Cheng (p. 873) shows how using drug knowledgebase information to distinguish between commonly confused drugs can prevent errors, and Vajravelu (p. 780) proposes a new algorithm to analyze multiple pharmacologic exposures using EHR data. Additionally, Samwald (p. 895) shares the experience of implementing pharmacogenomics decision support across seven European countries. The domains in which information systems can improve patient safety are numerous. Waters (p. 901) studies current use, interest, and perceived usability of clinical pathways for primary care, while Sittig (p. 915) describes the levels of adherence to recommended EHR safety practices across eight healthcare organizations. EHRbased intervention and reports on several safety topics are also presented in this issue of JAMIA: Ray (p. 863) uses statistical anomaly detection models for decision support system malfunctions, Chen (p. 790) analyzes interaction patterns of trauma providers that are associated with increased patients’ lengths of stay in the hospital, while Vahdat (p. 827) reports on a simulation study of the effects of EHR implementation on timeliness of care in a dermatology clinic. Berger (p. 833) integrates physical abuse measures into a pediatric clinical decision support system, while Meyer (p. 841) evaluates a mobile application to improve clinical laboratory test ordering. The applications and algorithms described in this issue of JAMIA would be hard to implement without standardization of terminologies, ontologies, and foundational research in natural language processing and information retrieval. Examples of advances in these areas are also featured: Cuzzola (p. 819) links UMLS to DBpedia to promote knowledge discovery, Wang (p. 809) describes efforts involving RxNorm that are leading to a normalized clinical drug knowledge base in China, Vreeman (p. 886) presents a unified terminology for radiology procedures (the “LOINC RSNA Radiology Playbook”), and Blosnich (p. 907) sho","PeriodicalId":344533,"journal":{"name":"J. Am. Medical Informatics Assoc.","volume":"83 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2018-06-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"129342836","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
The adoption of electronic health records (EHRs) in the U.S. was greatly accelerated by the HITECH “meaningful use” (MU) regulations, which require that all healthcare institutions either implement these systems or pay penalties. Accordingly, in the past decade, JAMIA has received many more manuscripts focused on the experiences from selecting, customizing, implementing, and evaluating the use of EHRs in various clinical settings, and on exchanging information contained in the EHRs across healthcare institutions. This issue of JAMIA focuses on the latest experiences of using and evaluating EHRs and a health information exchange (HIE). Understanding where EHRs can be improved is an important factor in their continued adoption. Goss (p. 661) proposes a value set for adverse reaction documentation, and Kannampallil (p. 739) describes the association between issuing medication orders for the wrong patient and the number of open charts in a clinician’s monitor. Wright (p. 709) explains how free-text electronic prescriptions can result in communication failure, and Percha (p. 679) proposes an expansion of the radiology lexicon using contextual patterns contained in radiology reports. The importance of customizing EHR systems to clinical workflows in different settings has been extensively documented in the biomedical informatics literature. Veinot (p. 746) describes a process to model clinical information interactions in primary care, and Ramelson (p. 715) reports on an enhanced referral management system. Krousel-Wood (p. 618) compares healthcare provider perceptions on transitioning from a small EHR system into a comprehensive commercial system. Price-Haywood (p. 702) analyzes dose effects of communication between patients and the care team via secure portal messaging, and Reading (p. 759) reports on the converging and diverging needs among patients and providers who are using patient-generated health data. In addition to their role of assisting clinicians in documenting their activities and using the information to provide care, EHR systems have an important role for healthcare quality, management, and biomedical research. Cho (p. 730) reports on how specific eMeasurements can be automatically populated from EHR systems. Holman (p. 694) describes how MU can result in both benefits and burdens for family physicians, Holmgren (p. 654) assesses the relationship between specific EHR systems and MU performance. Additionally, Casucci (p. 670) uses Medicaid data to study effects of chronic disease combinations on 30-day hospital readmissions, an important healthcare quality measure. Fraser (p. 627) discusses barriers to the success of an electronic pharmacovigilance system, and Baron (p. 645) proposes an approach for imputing multi-analyte values in longitudinal clinical data for use in machine learning systems. In an era where healthcare data integration becomes the norm, several HIE approaches are being pursued across counties, states, and nations. Motul
{"title":"Electronic health records and health information exchange","authors":"L. Ohno-Machado","doi":"10.1093/jamia/ocy057","DOIUrl":"https://doi.org/10.1093/jamia/ocy057","url":null,"abstract":"The adoption of electronic health records (EHRs) in the U.S. was greatly accelerated by the HITECH “meaningful use” (MU) regulations, which require that all healthcare institutions either implement these systems or pay penalties. Accordingly, in the past decade, JAMIA has received many more manuscripts focused on the experiences from selecting, customizing, implementing, and evaluating the use of EHRs in various clinical settings, and on exchanging information contained in the EHRs across healthcare institutions. This issue of JAMIA focuses on the latest experiences of using and evaluating EHRs and a health information exchange (HIE). Understanding where EHRs can be improved is an important factor in their continued adoption. Goss (p. 661) proposes a value set for adverse reaction documentation, and Kannampallil (p. 739) describes the association between issuing medication orders for the wrong patient and the number of open charts in a clinician’s monitor. Wright (p. 709) explains how free-text electronic prescriptions can result in communication failure, and Percha (p. 679) proposes an expansion of the radiology lexicon using contextual patterns contained in radiology reports. The importance of customizing EHR systems to clinical workflows in different settings has been extensively documented in the biomedical informatics literature. Veinot (p. 746) describes a process to model clinical information interactions in primary care, and Ramelson (p. 715) reports on an enhanced referral management system. Krousel-Wood (p. 618) compares healthcare provider perceptions on transitioning from a small EHR system into a comprehensive commercial system. Price-Haywood (p. 702) analyzes dose effects of communication between patients and the care team via secure portal messaging, and Reading (p. 759) reports on the converging and diverging needs among patients and providers who are using patient-generated health data. In addition to their role of assisting clinicians in documenting their activities and using the information to provide care, EHR systems have an important role for healthcare quality, management, and biomedical research. Cho (p. 730) reports on how specific eMeasurements can be automatically populated from EHR systems. Holman (p. 694) describes how MU can result in both benefits and burdens for family physicians, Holmgren (p. 654) assesses the relationship between specific EHR systems and MU performance. Additionally, Casucci (p. 670) uses Medicaid data to study effects of chronic disease combinations on 30-day hospital readmissions, an important healthcare quality measure. Fraser (p. 627) discusses barriers to the success of an electronic pharmacovigilance system, and Baron (p. 645) proposes an approach for imputing multi-analyte values in longitudinal clinical data for use in machine learning systems. In an era where healthcare data integration becomes the norm, several HIE approaches are being pursued across counties, states, and nations. Motul","PeriodicalId":344533,"journal":{"name":"J. Am. Medical Informatics Assoc.","volume":"36 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2018-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"132016570","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
{"title":"Enabling patients to be active participants in healthcare via informatics interventions","authors":"L. Ohno-Machado","doi":"10.1093/jamia/ocy019","DOIUrl":"https://doi.org/10.1093/jamia/ocy019","url":null,"abstract":"","PeriodicalId":344533,"journal":{"name":"J. Am. Medical Informatics Assoc.","volume":"47 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2018-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"124021612","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
{"title":"Informatics systems for health care providers, patients, and families","authors":"L. Ohno-Machado","doi":"10.1093/jamia/ocy001","DOIUrl":"https://doi.org/10.1093/jamia/ocy001","url":null,"abstract":"","PeriodicalId":344533,"journal":{"name":"J. Am. Medical Informatics Assoc.","volume":"11 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2018-02-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"131415580","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
{"title":"Understanding and mitigating the digital divide in health care","authors":"L. Ohno-Machado","doi":"10.1093/jamia/ocx082","DOIUrl":"https://doi.org/10.1093/jamia/ocx082","url":null,"abstract":"","PeriodicalId":344533,"journal":{"name":"J. Am. Medical Informatics Assoc.","volume":"34 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2017-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"125043207","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
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