{"title":"电子健康记录功能对初级保健中抑郁症治疗模式的影响","authors":"Elizabeth B Matthews, Ayse Akincigil","doi":"10.1080/17538157.2021.1990933","DOIUrl":null,"url":null,"abstract":"<p><strong>Background: </strong>Many individuals with depression are not being linked to treatment by their primary care providers. Electronic health records (EHRs) are common in medicine, but their impact on depression treatment is mixed. Because EHRs are diverse, differences may be attributable to differences in functionality. This study examines the relationship between EHR functions, and patterns of depression treatment in primary care.</p><p><strong>Methods: </strong>secondary analyses from the 2013-2016 National Ambulatory Medical Care Survey examined adult primary care patients with new or acute depression (n = 5,368). Bivariate comparisons examined patterns of depression treatment by general EHR use, and logistic regression examined the impact of individual EHR functions on treatment receipt.</p><p><strong>Results: </strong>Half the sample (57%; N = 3,034) was linked to depression treatment. Of this, 98.5% (n = 2,985) were prescribed antidepressants, while 4.3% (n = 130) were linked to mental health. EHR use did not impact mental health linkages, but EHR functions did affect antidepressant prescribing. Medication reconciliation decreased the odds of receiving an antidepressant (OR = .60, <i>p</i> < .05), while contraindication warnings increased the likelihood of an antidepressant prescription (OR = 1.91, <i>p</i> < .001).</p><p><strong>Conclusions: </strong>EHR systems did not impact mental health linkages but improved rates of antidepressant prescribing. Optimizing the use of contraindication warnings may be a key mechanism to encourage antidepressant treatment.</p>","PeriodicalId":54984,"journal":{"name":"Informatics for Health & Social Care","volume":null,"pages":null},"PeriodicalIF":2.5000,"publicationDate":"2022-07-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"1","resultStr":"{\"title\":\"The impact of electronic health record functions on patterns of depression treatment in primary care.\",\"authors\":\"Elizabeth B Matthews, Ayse Akincigil\",\"doi\":\"10.1080/17538157.2021.1990933\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"<p><strong>Background: </strong>Many individuals with depression are not being linked to treatment by their primary care providers. Electronic health records (EHRs) are common in medicine, but their impact on depression treatment is mixed. Because EHRs are diverse, differences may be attributable to differences in functionality. This study examines the relationship between EHR functions, and patterns of depression treatment in primary care.</p><p><strong>Methods: </strong>secondary analyses from the 2013-2016 National Ambulatory Medical Care Survey examined adult primary care patients with new or acute depression (n = 5,368). Bivariate comparisons examined patterns of depression treatment by general EHR use, and logistic regression examined the impact of individual EHR functions on treatment receipt.</p><p><strong>Results: </strong>Half the sample (57%; N = 3,034) was linked to depression treatment. Of this, 98.5% (n = 2,985) were prescribed antidepressants, while 4.3% (n = 130) were linked to mental health. EHR use did not impact mental health linkages, but EHR functions did affect antidepressant prescribing. Medication reconciliation decreased the odds of receiving an antidepressant (OR = .60, <i>p</i> < .05), while contraindication warnings increased the likelihood of an antidepressant prescription (OR = 1.91, <i>p</i> < .001).</p><p><strong>Conclusions: </strong>EHR systems did not impact mental health linkages but improved rates of antidepressant prescribing. Optimizing the use of contraindication warnings may be a key mechanism to encourage antidepressant treatment.</p>\",\"PeriodicalId\":54984,\"journal\":{\"name\":\"Informatics for Health & Social Care\",\"volume\":null,\"pages\":null},\"PeriodicalIF\":2.5000,\"publicationDate\":\"2022-07-03\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"1\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Informatics for Health & Social Care\",\"FirstCategoryId\":\"3\",\"ListUrlMain\":\"https://doi.org/10.1080/17538157.2021.1990933\",\"RegionNum\":4,\"RegionCategory\":\"医学\",\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"2021/10/21 0:00:00\",\"PubModel\":\"Epub\",\"JCR\":\"Q2\",\"JCRName\":\"HEALTH CARE SCIENCES & SERVICES\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Informatics for Health & Social Care","FirstCategoryId":"3","ListUrlMain":"https://doi.org/10.1080/17538157.2021.1990933","RegionNum":4,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"2021/10/21 0:00:00","PubModel":"Epub","JCR":"Q2","JCRName":"HEALTH CARE SCIENCES & SERVICES","Score":null,"Total":0}
The impact of electronic health record functions on patterns of depression treatment in primary care.
Background: Many individuals with depression are not being linked to treatment by their primary care providers. Electronic health records (EHRs) are common in medicine, but their impact on depression treatment is mixed. Because EHRs are diverse, differences may be attributable to differences in functionality. This study examines the relationship between EHR functions, and patterns of depression treatment in primary care.
Methods: secondary analyses from the 2013-2016 National Ambulatory Medical Care Survey examined adult primary care patients with new or acute depression (n = 5,368). Bivariate comparisons examined patterns of depression treatment by general EHR use, and logistic regression examined the impact of individual EHR functions on treatment receipt.
Results: Half the sample (57%; N = 3,034) was linked to depression treatment. Of this, 98.5% (n = 2,985) were prescribed antidepressants, while 4.3% (n = 130) were linked to mental health. EHR use did not impact mental health linkages, but EHR functions did affect antidepressant prescribing. Medication reconciliation decreased the odds of receiving an antidepressant (OR = .60, p < .05), while contraindication warnings increased the likelihood of an antidepressant prescription (OR = 1.91, p < .001).
Conclusions: EHR systems did not impact mental health linkages but improved rates of antidepressant prescribing. Optimizing the use of contraindication warnings may be a key mechanism to encourage antidepressant treatment.
期刊介绍:
Informatics for Health & Social Care promotes evidence-based informatics as applied to the domain of health and social care. It showcases informatics research and practice within the many and diverse contexts of care; it takes personal information, both its direct and indirect use, as its central focus.
The scope of the Journal is broad, encompassing both the properties of care information and the life-cycle of associated information systems.
Consideration of the properties of care information will necessarily include the data itself, its representation, structure, and associated processes, as well as the context of its use, highlighting the related communication, computational, cognitive, social and ethical aspects.
Consideration of the life-cycle of care information systems includes full range from requirements, specifications, theoretical models and conceptual design through to sustainable implementations, and the valuation of impacts. Empirical evidence experiences related to implementation are particularly welcome.
Informatics in Health & Social Care seeks to consolidate and add to the core knowledge within the disciplines of Health and Social Care Informatics. The Journal therefore welcomes scientific papers, case studies and literature reviews. Examples of novel approaches are particularly welcome. Articles might, for example, show how care data is collected and transformed into useful and usable information, how informatics research is translated into practice, how specific results can be generalised, or perhaps provide case studies that facilitate learning from experience.