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<!DOCTYPE html><html lang="en-US"><head><meta charSet="utf-8"/><meta name="citation_title" content="Reducing Physicians’ Cognitive Load During Chart Review: A Problem-Oriented Summary of the Patient Electronic Record"/><meta name="citation_author" content="Jennifer J. 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7z"></path></svg></button></div></header></div></div><main id="main-content" class="_95E0P"><article class="elg8D"><div class="VkINb"><div class="aVLxf"><header class="yCh87 xMcIn"><div class="uxPqZ"><div class="jmC80"><div class="WgDvq"><div class="nOEv0">Publication</div><div class="_5uNw_">AMIA Annual Symposium 2021</div><div class="DRJNG">Conference paper</div></div><h1 class="O2DSC">Reducing Physicians’ Cognitive Load During Chart Review: A Problem-Oriented Summary of the Patient Electronic Record</h1></div><div class="rwWv4"><a class="kzzKE" href="/publications?source-instance=373">AMIA Annual Symposium 2021</a></div><a class="bPr5E fFsRH bx--btn bx--btn--xl bx--btn--secondary" href="https://pubmed.ncbi.nlm.nih.gov/35308927/">View publication<svg focusable="false" preserveAspectRatio="xMidYMid meet" xmlns="http://www.w3.org/2000/svg" fill="currentColor" aria-hidden="true" style="--cds-icon-size-01:1.5rem" width="24" height="24" viewBox="0 0 32 32" class="bx--btn__icon"><path 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information within electronic health records (EHRs) has resulted in a need for automated systems to mitigate the cognitive burden on physicians utilizing today’s EHR systems. We present ProSPER, a Problem-oriented Summary of the Patient Electronic Record that displays a patient summary centered around an autogenerated problem list and disease-specific views for chronic conditions. ProSPER was developed using 1,500 longitudinal patient records from two large multi-specialty medical groups in the United States, and leverages multiple natural language processing (NLP) components targeting various fundamental (e.g. syntactic analysis), clinical (e.g. adverse drug event extraction) and summarizing (e.g. problem list generation) tasks. We report evaluation results for each component and discuss how specific components address existing physician challenges in reviewing EHR data. This work demonstrates the need to leverage holistic information in EHRs to build a comprehensive summarization application, and the potential for NLP-based applications to support physicians and improve clinical care.</p></section></div><div class="IrjA7"><nav aria-label="breadcrumbs" class="g9di5 r3uz9"><ol><li><a class="bx--link" href="/">Home</a></li><li><span class="aEWzI" aria-hidden="true">↳ </span><a class="bx--link" href="/publications">Publications</a></li></ol></nav><section class="_8PIse izDYk"><h2 class="DGVSD">Date<svg focusable="false" preserveAspectRatio="xMidYMid meet" xmlns="http://www.w3.org/2000/svg" fill="currentColor" width="24" height="24" viewBox="0 0 32 32" aria-hidden="true"><path d="M16 22L6 12 7.4 10.6 16 19.2 24.6 10.6 26 12z"></path></svg></h2><time dateTime="2021-10-29T00:00:00.000Z">29 Oct 2021</time></section><section class="Yh30P izDYk"><h2 class="DGVSD">Publication<svg focusable="false" preserveAspectRatio="xMidYMid meet" xmlns="http://www.w3.org/2000/svg" fill="currentColor" width="24" height="24" viewBox="0 0 32 32" aria-hidden="true"><path d="M16 22L6 12 7.4 10.6 16 19.2 24.6 10.6 26 12z"></path></svg></h2><a class="bx--link" href="/publications?source-instance=373">AMIA Annual Symposium 2021</a></section><div class="xRbfW"></div><div class="WotuJ HpoGw"><div class="yJoMR"><section id="authors" class="izDYk"><h2 class="DGVSD">Authors<svg focusable="false" preserveAspectRatio="xMidYMid meet" xmlns="http://www.w3.org/2000/svg" fill="currentColor" width="24" height="24" viewBox="0 0 32 32" aria-hidden="true"><path d="M16 22L6 12 7.4 10.6 16 19.2 24.6 10.6 26 12z"></path></svg></h2><ul class="XBsQU qcmp9 _1PgYZ uvfUZ _0CM3a o0Z_b" style="--gap-sm:0.5rem;--gap-md:0.5rem;--gap-lg:0.5rem;--gap-xlg:0.5rem;--gap-max:0.5rem"><li class="iHbnT X4Y7w mBUmN FbtRo _8UAmi bL2oU"><a class="bx--tag ZvHdV undefined _7_FxK _9ckLP bx--tag--blue bx--tag--interactive" id="author-4531" href="/publications?author=4531"><span title="Jennifer J. Liang">Jennifer J. Liang</span></a></li><li class="iHbnT X4Y7w mBUmN FbtRo _8UAmi bL2oU"><a class="bx--tag ZvHdV undefined _7_FxK _9ckLP bx--tag--blue bx--tag--interactive" id="author-30503" href="/publications?author=30503"><span title="Ching-Huei Tsou">Ching-Huei Tsou</span></a></li><li class="iHbnT X4Y7w mBUmN FbtRo _8UAmi bL2oU"><a class="bx--tag ZvHdV undefined _7_FxK _9ckLP bx--tag--blue bx--tag--interactive" id="author-33515" href="/publications?author=33515"><span title="Bharath Dandala">Bharath Dandala</span></a></li><li class="iHbnT X4Y7w mBUmN FbtRo _8UAmi bL2oU"><a class="bx--tag ZvHdV undefined _7_FxK _9ckLP bx--tag--blue bx--tag--interactive" id="author-33516" href="/publications?author=33516"><span title="Ananya Poddar">Ananya Poddar</span></a></li><li class="iHbnT X4Y7w mBUmN FbtRo _8UAmi bL2oU"><a class="bx--tag ZvHdV undefined _7_FxK _9ckLP bx--tag--blue bx--tag--interactive" id="author-33517" href="/publications?author=33517"><span title="Venkata Naga Sreeram Joopudi">Venkata Naga Sreeram Joopudi</span></a></li><li class="iHbnT X4Y7w mBUmN FbtRo _8UAmi bL2oU"><a class="bx--tag ZvHdV undefined _7_FxK _9ckLP bx--tag--blue bx--tag--interactive" id="author-4669" href="/publications?author=4669"><span title="Diwakar Mahajan">Diwakar Mahajan</span></a></li><li class="iHbnT X4Y7w mBUmN FbtRo _8UAmi bL2oU"><a class="bx--tag ZvHdV undefined _7_FxK _9ckLP bx--tag--gray bx--tag--interactive" id="author-33518" href="/publications?author=33518"><span title="John Prager">John Prager</span></a></li><li class="iHbnT X4Y7w mBUmN FbtRo _8UAmi bL2oU"><a class="bx--tag ZvHdV undefined _7_FxK _9ckLP bx--tag--gray bx--tag--interactive" id="author-33519" href="/publications?author=33519"><span title="Preethi Raghavan">Preethi Raghavan</span></a></li><li class="iHbnT X4Y7w mBUmN FbtRo _8UAmi bL2oU"><a class="bx--tag ZvHdV undefined _7_FxK _9ckLP bx--tag--blue bx--tag--interactive" id="author-33520" href="/publications?author=33520"><span title="Michele Payne (Cestone)">Michele Payne (Cestone)</span></a></li></ul><div id="author-is-ibm-affiliated" role="presentation" class="sBunA"><svg width="16" height="16" viewBox="0 0 16 16" fill="none" role="presentation"><circle cx="8" cy="8" r="8" fill="#edf5ff"></circle><circle cx="8" cy="8" r="2" fill="#0f62fe"></circle></svg><span>IBM-affiliated at time of publication</span></div></section><section id="tags" class="izDYk"><h2 class="DGVSD">Topics<svg focusable="false" preserveAspectRatio="xMidYMid meet" xmlns="http://www.w3.org/2000/svg" fill="currentColor" width="24" height="24" viewBox="0 0 32 32" aria-hidden="true"><path d="M16 22L6 12 7.4 10.6 16 19.2 24.6 10.6 26 12z"></path></svg></h2><ul class="XBsQU qcmp9 _1PgYZ uvfUZ _0CM3a o0Z_b" style="--gap-sm:0.5rem;--gap-md:0.5rem;--gap-lg:0.5rem;--gap-xlg:0.5rem;--gap-max:0.5rem"><li class="iHbnT X4Y7w mBUmN FbtRo _8UAmi bL2oU"><a class="bx--tag ZvHdV sCEvD _7_FxK _9ckLP bx--tag--green bx--tag--interactive" id="25" href="/topics/natural-language-processing"><span 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class="rndoq"><svg focusable="false" preserveAspectRatio="xMidYMid meet" xmlns="http://www.w3.org/2000/svg" fill="currentColor" aria-hidden="true" width="24" height="24" viewBox="0 0 32 32" class="CjjQi"><path d="M25.7,9.3l-7-7C18.5,2.1,18.3,2,18,2H8C6.9,2,6,2.9,6,4v24c0,1.1,0.9,2,2,2h16c1.1,0,2-0.9,2-2V10C26,9.7,25.9,9.5,25.7,9.3 z M18,4.4l5.6,5.6H18V4.4z M24,28H8V4h8v6c0,1.1,0.9,2,2,2h6V28z"></path><path d="M10 22H22V24H10zM10 16H22V18H10z"></path></svg><div class="sXPlN"><div tabindex="0" class="L4C9a" aria-label="Abstract excerpt" role="region"><svg focusable="false" preserveAspectRatio="xMidYMid meet" xmlns="http://www.w3.org/2000/svg" fill="currentColor" aria-hidden="true" width="24" height="24" viewBox="0 0 32 32"><path d="M17 22L17 14 13 14 13 16 15 16 15 22 12 22 12 24 20 24 20 22 17 22zM16 8a1.5 1.5 0 101.5 1.5A1.5 1.5 0 0016 8z"></path><path d="M16,30A14,14,0,1,1,30,16,14,14,0,0,1,16,30ZM16,4A12,12,0,1,0,28,16,12,12,0,0,0,16,4Z"></path></svg></div></div></div><div class="UTCr9"><div class="c9tVS">Conference paper</div><h3 class="sa_bq"><a href="/publications/xnlp-a-living-survey-for-xai-research-in-natural-language-processing">XNLP: A living survey for XAI research in natural language processing</a></h3></div><footer class="SIZ95"><p>Kun Qian, Marina Danilevsky, et al.</p><p class="XErfb">IUI 2021</p></footer><svg focusable="false" preserveAspectRatio="xMidYMid meet" xmlns="http://www.w3.org/2000/svg" fill="currentColor" aria-hidden="true" width="24" height="24" viewBox="0 0 24 24" class="dgEn3"><path d="M14 4L12.9 5.1 18.9 11.2 2 11.2 2 12.8 18.9 12.8 12.9 18.9 14 20 22 12z"></path></svg></div></article><article class="E8N0J _9GOJO"><div class="_2AUU"><div class="rndoq"><svg focusable="false" preserveAspectRatio="xMidYMid meet" xmlns="http://www.w3.org/2000/svg" fill="currentColor" aria-hidden="true" width="24" height="24" viewBox="0 0 32 32" class="CjjQi"><path 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automation data</a></h3></div><footer class="SIZ95"><p>Xue Han, Lianxue Hu, et al.</p><p class="XErfb">SCC 2020</p></footer><svg focusable="false" preserveAspectRatio="xMidYMid meet" xmlns="http://www.w3.org/2000/svg" fill="currentColor" aria-hidden="true" width="24" height="24" viewBox="0 0 24 24" class="dgEn3"><path d="M14 4L12.9 5.1 18.9 11.2 2 11.2 2 12.8 18.9 12.8 12.9 18.9 14 20 22 12z"></path></svg></div></article><article class="E8N0J _9GOJO"><div class="_2AUU"><div class="rndoq"><svg focusable="false" preserveAspectRatio="xMidYMid meet" xmlns="http://www.w3.org/2000/svg" fill="currentColor" aria-hidden="true" width="24" height="24" viewBox="0 0 32 32" class="CjjQi"><path d="M25.7,9.3l-7-7C18.5,2.1,18.3,2,18,2H8C6.9,2,6,2.9,6,4v24c0,1.1,0.9,2,2,2h16c1.1,0,2-0.9,2-2V10C26,9.7,25.9,9.5,25.7,9.3 z M18,4.4l5.6,5.6H18V4.4z M24,28H8V4h8v6c0,1.1,0.9,2,2,2h6V28z"></path><path d="M10 22H22V24H10zM10 16H22V18H10z"></path></svg><div class="sXPlN"><div tabindex="0" class="L4C9a" aria-label="Abstract excerpt" role="region"><svg focusable="false" preserveAspectRatio="xMidYMid meet" xmlns="http://www.w3.org/2000/svg" fill="currentColor" aria-hidden="true" width="24" height="24" viewBox="0 0 32 32"><path d="M17 22L17 14 13 14 13 16 15 16 15 22 12 22 12 24 20 24 20 22 17 22zM16 8a1.5 1.5 0 101.5 1.5A1.5 1.5 0 0016 8z"></path><path d="M16,30A14,14,0,1,1,30,16,14,14,0,0,1,16,30ZM16,4A12,12,0,1,0,28,16,12,12,0,0,0,16,4Z"></path></svg></div></div></div><div class="UTCr9"><div class="c9tVS">Conference paper</div><h3 class="sa_bq"><a href="/publications/do-not-have-enough-data-deep-learning-to-the-rescue">Do not have enough data? Deep learning to the rescue!</a></h3></div><footer class="SIZ95"><p>Ateret Anaby Tavor, Boaz Carmeli, et al.</p><p class="XErfb">AAAI 2020</p></footer><svg focusable="false" preserveAspectRatio="xMidYMid meet" xmlns="http://www.w3.org/2000/svg" fill="currentColor" aria-hidden="true" width="24" height="24" viewBox="0 0 24 24" class="dgEn3"><path d="M14 4L12.9 5.1 18.9 11.2 2 11.2 2 12.8 18.9 12.8 12.9 18.9 14 20 22 12z"></path></svg></div></article><article class="E8N0J _9GOJO"><div class="_2AUU"><div class="rndoq"><svg focusable="false" preserveAspectRatio="xMidYMid meet" xmlns="http://www.w3.org/2000/svg" fill="currentColor" aria-hidden="true" width="24" height="24" viewBox="0 0 32 32" class="CjjQi"><path d="M25.7,9.3l-7-7C18.5,2.1,18.3,2,18,2H8C6.9,2,6,2.9,6,4v24c0,1.1,0.9,2,2,2h16c1.1,0,2-0.9,2-2V10C26,9.7,25.9,9.5,25.7,9.3 z M18,4.4l5.6,5.6H18V4.4z M24,28H8V4h8v6c0,1.1,0.9,2,2,2h6V28z"></path><path d="M10 22H22V24H10zM10 16H22V18H10z"></path></svg><div class="sXPlN"><a aria-label="Code available" class="fFsRH bx--btn bx--btn--ghost bx--btn--icon-only" href="https://github.com/IBM/PoWER-BERT"><svg focusable="false" preserveAspectRatio="xMidYMid meet" xmlns="http://www.w3.org/2000/svg" fill="currentColor" aria-hidden="true" aria-label="Code available" style="--cds-icon-size-01:1.5rem" width="24" height="24" viewBox="0 0 32 32" role="img" class="bx--btn__icon"><path d="M31 16L24 23 22.59 21.59 28.17 16 22.59 10.41 24 9 31 16zM1 16L8 9 9.41 10.41 3.83 16 9.41 21.59 8 23 1 16z"></path><path d="M5.91 15H26.080000000000002V17H5.91z" transform="rotate(-75 15.996 16)"></path></svg></a><div tabindex="0" class="L4C9a" aria-label="Abstract excerpt" role="region"><svg focusable="false" preserveAspectRatio="xMidYMid meet" xmlns="http://www.w3.org/2000/svg" fill="currentColor" aria-hidden="true" width="24" height="24" viewBox="0 0 32 32"><path d="M17 22L17 14 13 14 13 16 15 16 15 22 12 22 12 24 20 24 20 22 17 22zM16 8a1.5 1.5 0 101.5 1.5A1.5 1.5 0 0016 8z"></path><path d="M16,30A14,14,0,1,1,30,16,14,14,0,0,1,16,30ZM16,4A12,12,0,1,0,28,16,12,12,0,0,0,16,4Z"></path></svg></div></div></div><div class="UTCr9"><div class="c9tVS">Conference paper</div><h3 class="sa_bq"><a href="/publications/power-bert-accelerating-bert-inference-via-progressive-word-vector-elimination">PoWER-BERT: Accelerating BERT inference via progressive word-vector elimination</a></h3></div><footer class="SIZ95"><p>Saurabh Goyal, Anamitra R. Choudhury, et al.</p><p class="XErfb">ICML 2020</p></footer><svg focusable="false" preserveAspectRatio="xMidYMid meet" xmlns="http://www.w3.org/2000/svg" fill="currentColor" aria-hidden="true" width="24" height="24" viewBox="0 0 24 24" class="dgEn3"><path d="M14 4L12.9 5.1 18.9 11.2 2 11.2 2 12.8 18.9 12.8 12.9 18.9 14 20 22 12z"></path></svg></div></article><footer class="Fyp_Y"><a class="fFsRH bx--btn bx--btn--secondary" href="/publications">View all publications<svg focusable="false" preserveAspectRatio="xMidYMid meet" xmlns="http://www.w3.org/2000/svg" fill="currentColor" aria-hidden="true" style="--cds-icon-size-01:1.5rem" width="24" height="24" viewBox="0 0 24 24" class="bx--btn__icon"><path d="M14 4L12.9 5.1 18.9 11.2 2 11.2 2 12.8 18.9 12.8 12.9 18.9 14 20 22 12z"></path></svg></a></footer></div></section></div></footer></article></main><footer data-autoid="dds--footer" class="bx--footer"><section class="bx--footer__main"><div class="bx--footer__main-container"><div 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It works by: a) exploiting redundancy pertaining to word-vectors (intermediate transformer block outputs) and eliminating the redundant vectors. b) determining which word-vectors to eliminate by developing a strategy for measuring their significance, based on the self-attention mechanism. c) learning how many word-vectors to eliminate by augmenting the BERT model and the loss function. Experiments on the standard GLUE benchmark shows that PoWER-BERT achieves up to 4.5x reduction in inference time over BERT with \u003c 1% loss in accuracy. We show that PoWER-BERT offers significantly better trade-off between accuracy and inference time compared to prior methods. We demonstrate that our method attains up to 6.8x reduction in inference time with \u003c 1% loss in accuracy when applied over ALBERT, a highly compressed version of BERT. The code for PoWER-BERT is publicly available at https://github.com/IBM/PoWER-BERT.","linkCode":"https://github.com/IBM/PoWER-BERT","source":{"__ref":"Source:445"},"sourceInstance":{"__ref":"SourceInstance:257"},"authors":[{"__typename":"PublicationHasAuthorName","authorName":{"__ref":"AuthorName:3546"},"affiliations":[{"__ref":"Affiliation:6"}]},{"__typename":"PublicationHasAuthorName","authorName":{"__ref":"AuthorName:3556"},"affiliations":[{"__ref":"Affiliation:6"}]},{"__typename":"PublicationHasAuthorName","authorName":{"__ref":"AuthorName:17017"},"affiliations":[{"__ref":"Affiliation:6"}]},{"__typename":"PublicationHasAuthorName","authorName":{"__ref":"AuthorName:3547"},"affiliations":[{"__ref":"Affiliation:6"}]},{"__typename":"PublicationHasAuthorName","authorName":{"__ref":"AuthorName:3548"},"affiliations":[{"__ref":"Affiliation:6"}]},{"__typename":"PublicationHasAuthorName","authorName":{"__ref":"AuthorName:3549"},"affiliations":[{"__ref":"Affiliation:6"}]}]},"Publication:10953":{"__typename":"Publication","id":"10953","slug":"reducing-physicians-cognitive-load-during-chart-review-a-problem-oriented-summary-of-the-patient-electronic-record","title":"Reducing Physicians’ Cognitive Load During Chart Review: A Problem-Oriented Summary of the Patient Electronic Record","type":{"__typename":"PublicationType","displayValue":"Conference paper"},"published":"2021-10-29","publishedMeta":{"__typename":"PublishedMeta","source":"AMIA Annual Symposium 2021","year":"2021"},"abstract":"Overabundance of information within electronic health records (EHRs) has resulted in a need for automated systems to mitigate the cognitive burden on physicians utilizing today’s EHR systems. We present ProSPER, a Problem-oriented Summary of the Patient Electronic Record that displays a patient summary centered around an autogenerated problem list and disease-specific views for chronic conditions. ProSPER was developed using 1,500 longitudinal patient records from two large multi-specialty medical groups in the United States, and leverages multiple natural language processing (NLP) components targeting various fundamental (e.g. syntactic analysis), clinical (e.g. adverse drug event extraction) and summarizing (e.g. problem list generation) tasks. We report evaluation results for each component and discuss how specific components address existing physician challenges in reviewing EHR data. This work demonstrates the need to leverage holistic information in EHRs to build a comprehensive summarization application, and the potential for NLP-based applications to support physicians and improve clinical care.","linkCode":null,"source":{"__ref":"Source:474"},"sourceInstance":{"__ref":"SourceInstance:373"},"authors":[{"__typename":"PublicationHasAuthorName","authorName":{"__ref":"AuthorName:4987"},"affiliations":[{"__ref":"Affiliation:73"}]},{"__typename":"PublicationHasAuthorName","authorName":{"__ref":"AuthorName:24932"},"affiliations":[{"__ref":"Affiliation:73"}]},{"__typename":"PublicationHasAuthorName","authorName":{"__ref":"AuthorName:27377"},"affiliations":[{"__ref":"Affiliation:73"}]},{"__typename":"PublicationHasAuthorName","authorName":{"__ref":"AuthorName:27378"},"affiliations":[{"__ref":"Affiliation:73"}]},{"__typename":"PublicationHasAuthorName","authorName":{"__ref":"AuthorName:27379"},"affiliations":[{"__ref":"Affiliation:73"}]},{"__typename":"PublicationHasAuthorName","authorName":{"__ref":"AuthorName:5127"},"affiliations":[{"__ref":"Affiliation:73"}]},{"__typename":"PublicationHasAuthorName","authorName":{"__ref":"AuthorName:27380"},"affiliations":[{"__ref":"Affiliation:3020"}]},{"__typename":"PublicationHasAuthorName","authorName":{"__ref":"AuthorName:27381"},"affiliations":[{"__ref":"Affiliation:3020"}]},{"__typename":"PublicationHasAuthorName","authorName":{"__ref":"AuthorName:27382"},"affiliations":[{"__ref":"Affiliation:73"}]}],"link":"https://pubmed.ncbi.nlm.nih.gov/35308927/","linkPdf":null,"linkVideo":null,"linkDemo":null,"linkDataset":null,"linkBlog":null,"linkSlides":null,"linkWebsite":null,"linkCaseStudy":null,"linkPoster":null,"bibtex":null,"isbn":null,"issn":null,"publisher":null,"doi":null,"notes":{"__typename":"PublicationNotesQuery","result":null},"tags":[{"__ref":"Tag:25"},{"__ref":"Tag:80"}],"related":[{"__ref":"Publication:42"},{"__ref":"Publication:220"},{"__ref":"Publication:625"},{"__ref":"Publication:661"}]},"ROOT_QUERY":{"__typename":"Query","publicationBySlug({\"slug\":\"reducing-physicians-cognitive-load-during-chart-review-a-problem-oriented-summary-of-the-patient-electronic-record\"})":{"__ref":"Publication:10953"}}}},"__N_SSP":true},"page":"/publications/[pid]","query":{"pid":"reducing-physicians-cognitive-load-during-chart-review-a-problem-oriented-summary-of-the-patient-electronic-record"},"buildId":"v9TvvervEOiCcTzEPxR9P","isFallback":false,"gssp":true,"locale":"en-US","locales":["en-US"],"defaultLocale":"en-US","scriptLoader":[]}</script></body></html>