What daily digital data makes visible<\/h2>\n
My lab works at the intersection of women\u2019s health, digital technologies, and data science. We work with three main types of information:<\/p>\n
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Mobile health (mHealth) apps where people record symptoms, pain flares, mood, menstrual cycles, and daily functioning in real time.<\/p>\n<\/li>\n
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Wearables that track steps, heart rate, sleep, and other physiological signals across everyday life.<\/p>\n<\/li>\n
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Clinical narratives in the electronic health record, where years of experiences are compressed into a few lines of text.<\/p>\n<\/li>\n<\/ul>\n
If you look only at a traditional medical chart, you see what the system is designed to capture: diagnoses, procedures, imaging, labs, occasional pain scores. It\u2019s a sparse series of dots on a timeline. When you add daily digital traces, the curve connecting those frequently sampled data points starts to come into view. And these curves (“functional data<\/a>“) are what we focus on that allow us to see patterns we would not see otherwise:<\/p>\n
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Within\u2011person cycles of pain, fatigue, and mood across the menstrual cycle that are invisible in annual snapshots.<\/p>\n<\/li>\n
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Distinct behavioral \u201cphenotypes\u201d: people who keep moving despite significant pain, people who sharply restrict activity, and people whose patterns change with work, caregiving, or stress.<\/p>\n<\/li>\n
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Early warning signatures of symptom escalation, like changes in sleep, movement, or stress that often precede a \u201cbad week.\u201d<\/p>\n<\/li>\n<\/ul>\n
When our data begin to match the temporal resolution of people\u2019s lives<\/strong>, what used to look like \u201cunpredictable pain\u201d starts to look like a pattern we can understand and sometimes anticipate. The conversation can turn from \u201cnothing is wrong; your tests are normal\u201d to \u201cthere is a pattern here; let\u2019s talk about what might be driving it and how to respond.\u201d<\/p>\n
AI as microscope, not oracle<\/h2>\n
Digital traces on their own are messy: irregular, noisy, and profoundly individual. This is where artificial intelligence when used judiciously can help us make sense of them. In our work, we use:<\/p>\n
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Unsupervised learning to discover symptom and behavior patterns without assuming in advance what \u201ctypes\u201d of patients exist.<\/p>\n<\/li>\n
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Reinforcement learning to personalize exercise recommendations over time for someone with chronic pelvic pain, learning from their feedback and context.<\/p>\n<\/li>\n
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Large language models to synthesize unstructured clinical notes and identify sub-types<\/a> of endometriosis or patients who might have conditions like endometriosis years before a formal diagnosis.<\/p>\n<\/li>\n<\/ul>\n
I think of these tools as a microscope, not an oracle<\/strong>. A microscope lets you see subtle structures that are invisible to the naked eye. It does not tell you what to value or how to care. In the same way, AI can surface patterns in years of symptom tracking and clinical notes; but it should not overrule clinical judgment or lived experience. There are also real risks if we are not careful. Models trained on historically incomplete data will reproduce those gaps. Over\u2011confident outputs can harden genuine uncertainty into premature \u201canswers,\u201d especially in areas where women\u2019s pain has already been minimized. Algorithms optimized purely for prediction can miss what matters most to patients: functioning, identity, relationships, dignity. The question is whether we will use AI and digital technologies to deepen listening, or old patterns.<\/p>\n
From data to justice<\/h2>\n
Underneath all of this is a question of epistemic justice:<\/strong> Who is recognized as a knower; whose experiences are treated as credible; whose words and signals count as evidence; who gets to shape the questions we ask. In women\u2019s reproductive health, epistemic injustice has been the norm. Pain and bleeding that disrupt work, school, and family life are framed as \u201cnormal.\u201d When tests come back \u201creassuring,\u201d patients are left feeling gaslit: their lived reality is in conflict with the medical record. Digital health can either reinforce these dynamics or start to correct them. When people use mHealth tools and wearables, they become co\u2011producers of data about their own bodies. But if those data disappear into opaque silos or are fed into models that no one can explain, we risk a new kind of dispossession: intimate traces of people\u2019s lives used, with little meaningful care in return.<\/p>\n
Closing the data gap in women\u2019s health therefore requires more than better algorithms. It requires a different relationship to data, grounded in three commitments:<\/p>\n
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- Measure what matters to patients<\/strong>, not only what is convenient for billing or devices.<\/li>\n
- Design with the most overlooked at the center<\/strong>, not as an afterthought.<\/li>\n
- Treat data as building blocks of relationship, not commodity. <\/strong>This goes back to my previous post on clear consent, shared interpretation, and real options to say no.<\/li>\n<\/ol>\n
A future for women’s health<\/h2>\n
I imagine a near\u2011future visit where a patient and clinician sit together to review several months of symptoms, activity, sleep, and mood, summarized in intuitive ways, not just in dense graphs. This means that regardless of how much AI integration there might be introduced to the process, making sense of those patterns in the context of a real life requires a conversation between the two humans.<\/p>\n
The goal is not to turn everyone into a quantified\u2011self enthusiast, or to automate away expertise. It is to build a system in which women\u2019s experiences are legible, believed, and actionable. We already have many of the tools we need. The open question, one that belongs at the center of any conversation about the future of health, is whether we will use those tools to listen better to women and to anyone whose suffering has been poorly captured by our current systems. That is the future my lab is working toward, one dataset and one conversation at a time.<\/p>\n
![\"\"](\"https:\/\/labs.icahn.mssm.edu\/ensarilab\/wp-content\/uploads\/sites\/503\/2026\/06\/cpptracker_new_big-1024x617.png\")
<\/p>\nNext Steps for CPP Tracker<\/h2>\n
All of these make me very excited for what is next for CPP Tracker, our study that aims to develop validated digital patient-reported outcome measures (dPROMs) specifically designed for those with CPPDs. Ever since we began enrollment back in 2023, I have been getting questions on when it will be scaled and available for everyone to download. We will soon make this research-based App available for everyone interested to download and start using to track their symptoms, health, and have summaries that they can use with their clinicians. It will continue to evolve and improve based on our participants’ feedback and needs, so the collaboration we have with our participants will continue. This is what makes this tool relevant and useful. Because digital health research depends on participant trust, and trust depends on clear communication.<\/p>\n
Closing: The Pondering Professor Corner<\/p>\n
This post is the fourth in our series The Pondering Professor Corner<\/em> about how we think about your data, your trust, and your participation in digital health research. You can read other posts in this series on informed consent<\/a> on research data use<\/a>, and on\u00a0patient trust with digital data<\/a><\/p>\n
\u2014
\nAbout the author: Ipek Ensari, PhD, is an Assistant Professor at the Icahn School of Medicine at Mount Sinai Department of AI and Human Health and principal investigator of the CPP Tracker study on chronic pelvic pain. She is a digital health researcher focused on gynecological pain disorders like endometriosis, adenomyosis, and fibroids, wearable and app-based data, and AI-driven methods, with a particular interest in health data privacy, informed consent, and participant-centered study design.<\/p>\n","protected":false},"excerpt":{"rendered":"The Pondering Professor \u2013 Reflections on digital data and trust from PI Ipek Ensari As a researcher whose investigative focus revolves around conditions like endometriosis, adenomyosis, fibroids, and other similar chronic pelvic pain related disorders (CPPDs), I kept hearing the same phrase for years: \u201cThese conditions are just mysterious.\u201d In practice, what people usually mean […]<\/p>\n","protected":false},"author":640,"featured_media":0,"comment_status":"closed","ping_status":"open","sticky":false,"template":"","format":"standard","meta":{"_et_pb_use_builder":"","_et_pb_old_content":"","_et_gb_content_width":"","inline_featured_image":false,"footnotes":""},"categories":[36,54,1],"tags":[],"class_list":["post-5606","post","type-post","status-publish","format-standard","hentry","category-digital-data","category-epistemic-justice","category-womens-health"],"aioseo_notices":[],"_links":{"self":[{"href":"https:\/\/labs.icahn.mssm.edu\/ensarilab\/wp-json\/wp\/v2\/posts\/5606","targetHints":{"allow":["GET"]}}],"collection":[{"href":"https:\/\/labs.icahn.mssm.edu\/ensarilab\/wp-json\/wp\/v2\/posts"}],"about":[{"href":"https:\/\/labs.icahn.mssm.edu\/ensarilab\/wp-json\/wp\/v2\/types\/post"}],"author":[{"embeddable":true,"href":"https:\/\/labs.icahn.mssm.edu\/ensarilab\/wp-json\/wp\/v2\/users\/640"}],"replies":[{"embeddable":true,"href":"https:\/\/labs.icahn.mssm.edu\/ensarilab\/wp-json\/wp\/v2\/comments?post=5606"}],"version-history":[{"count":6,"href":"https:\/\/labs.icahn.mssm.edu\/ensarilab\/wp-json\/wp\/v2\/posts\/5606\/revisions"}],"predecessor-version":[{"id":5630,"href":"https:\/\/labs.icahn.mssm.edu\/ensarilab\/wp-json\/wp\/v2\/posts\/5606\/revisions\/5630"}],"wp:attachment":[{"href":"https:\/\/labs.icahn.mssm.edu\/ensarilab\/wp-json\/wp\/v2\/media?parent=5606"}],"wp:term":[{"taxonomy":"category","embeddable":true,"href":"https:\/\/labs.icahn.mssm.edu\/ensarilab\/wp-json\/wp\/v2\/categories?post=5606"},{"taxonomy":"post_tag","embeddable":true,"href":"https:\/\/labs.icahn.mssm.edu\/ensarilab\/wp-json\/wp\/v2\/tags?post=5606"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}
- Design with the most overlooked at the center<\/strong>, not as an afterthought.<\/li>\n
- Measure what matters to patients<\/strong>, not only what is convenient for billing or devices.<\/li>\n
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