Title: The Variation in Patient Portal Access for Adolescents in the United States: How Different Medical Centers Manage their Adolescent Access
Adolescence is a time when patients are approaching autonomy, both developmentally and legally. Yet they are still minors and are likely to encounter contradictions between situations in which they are treated as children and ones in which they are treated as adults. Being able to access their medical information may enable adolescents to take on a participatory role in their health care.
However, federal policy, state law, and community norms are not consistent regarding adolescent healthcare and privacy. For example, in some regions and under some circumstances, adolescents may have consent and privacy rights similar to those of adults, with the right to make some, or all, of their own sensitive medical decisions privately. In other cases, parental notification is the norm, or guidance is unclear or lacking.
In the absence of national guidelines, medical centers encounter serious challenges when developing policies about adolescent access to medical records via patient portals. The American Academy of Pediatrics has made recommendations, but these are not binding.
To explore diversity in adolescent privacy policies and identify common approaches, we are conducting a qualitative study with key informants from different types of medical organizations in different regions of the country. The main objective is to identify diversity in adolescent privacy features within the patient portal. Another objective is to enumerate the factors involved in making portal access decisions. A third objective is to identify the potential need for more formalized guidance and standards on privacy features within the patient portal more »« less
Online patient portals become important during disruptions to in-person health care, like when cases of coronavirus disease 2019 (COVID-19) and other respiratory viruses rise, yet underlying structural inequalities associated with race, socio-economic status, and other socio-demographic characteristics may affect their use. We analyzed a population-based survey to identify disparities within the United States in access to online portals during the early period of COVID-19 in 2020.
Materials and Methods
The National Cancer Institute fielded the 2020 Health and Information National Trends Survey from February to June 2020. We conducted multivariable analysis to identify socio-demographic characteristics of US patients who were offered and accessed online portals, and reasons for nonuse.
Results
Less than half of insured adult patients reported accessing an online portal in the prior 12 months, and this was less common among patients who are male, are Hispanic, have less than a college degree, have Medicaid insurance, have no regular provider, or have no internet. Reasons for nonuse include: wanting to speak directly to a provider, not having an online record, concerns about privacy, and discomfort with technology.
Discussion
Despite the rapid expansion of digital health technologies due to COVID-19, we found persistent socio-demographic disparities in access to patient portals. Ensuring that digital health tools are secure, private, and trustworthy would address some patient concerns that are barriers to portal access.
Conclusion
Expanding the use of online portals requires explicitly addressing fundamental inequities to prevent exacerbating existing disparities, particularly during surges in cases of COVID-19 and other respiratory viruses that tax health care resources.
Wilcox, Lauren; Sharko, Marianne; Hong, Matthew; Hollberg, Julie; Ancker, Jessica(
, American Medical Informatics Association 2018 Annual Symposium)
Research examining whether and how adolescent patients should gain access to their electronic health records is gaining momentum. We conducted a survey to explore diversity in adolescent privacy policies and identify common approaches in health information technology management for adolescent patients. Through descriptive analyses of survey data, we found a wide range of institutional policies regarding adolescent patient privacy, and large variations in health IT executives’ baseline knowledge of access policies. A majority of respondents agreed that formal guidelines pertaining to adolescent health record privacy would be helpful. Respondents suggested that these guidelines can be developed through the synthesis of multiple perspectives, including those of pediatricians, adolescent specialists, privacy experts, parents, patient advocates, and other professional entities.
Wevodau, Z.; Doshna, B.; Jhala, N.; Akhtar, I.; Obeid, I.; Picone, J.(
, Proceedings of the IEEE Signal Processing in Medicine and Biology Symposium (SPMB))
Obeid, I.
(Ed.)
The Neural Engineering Data Consortium (NEDC) is developing the Temple University Digital Pathology Corpus (TUDP), an open source database of high-resolution images from scanned pathology samples [1], as part of its National Science Foundation-funded Major Research Instrumentation grant titled “MRI: High Performance Digital Pathology Using Big Data and Machine Learning” [2]. The long-term goal of this project is to release one million images. We have currently scanned over 100,000 images and are in the process of annotating breast tissue data for our first official corpus release, v1.0.0. This release contains 3,505 annotated images of breast tissue including 74 patients with cancerous diagnoses (out of a total of 296 patients). In this poster, we will present an analysis of this corpus and discuss the challenges we have faced in efficiently producing high quality annotations of breast tissue.
It is well known that state of the art algorithms in machine learning require vast amounts of data. Fields such as speech recognition [3], image recognition [4] and text processing [5] are able to deliver impressive performance with complex deep learning models because they have developed large corpora to support training of extremely high-dimensional models (e.g., billions of parameters). Other fields that do not have access to such data resources must rely on techniques in which existing models can be adapted to new datasets [6]. A preliminary version of this breast corpus release was tested in a pilot study using a baseline machine learning system, ResNet18 [7], that leverages several open-source Python tools.
The pilot corpus was divided into three sets: train, development, and evaluation. Portions of these slides were manually annotated [1] using the nine labels in Table 1 [8] to identify five to ten examples of pathological features on each slide. Not every pathological feature is annotated, meaning excluded areas can include focuses particular to these labels that are not used for training. A summary of the number of patches within each label is given in Table 2. To maintain a balanced training set, 1,000 patches of each label were used to train the machine learning model. Throughout all sets, only annotated patches were involved in model development.
The performance of this model in identifying all the patches in the evaluation set can be seen in the confusion matrix of classification accuracy in Table 3. The highest performing labels were background, 97% correct identification, and artifact, 76% correct identification. A correlation exists between labels with more than 6,000 development patches and accurate performance on the evaluation set. Additionally, these results indicated a need to further refine the annotation of invasive ductal carcinoma (“indc”), inflammation (“infl”), nonneoplastic features (“nneo”), normal (“norm”) and suspicious (“susp”).
This pilot experiment motivated changes to the corpus that will be discussed in detail in this poster presentation. To increase the accuracy of the machine learning model, we modified how we addressed underperforming labels. One common source of error arose with how non-background labels were converted into patches. Large areas of background within other labels were isolated within a patch resulting in connective tissue misrepresenting a non-background label. In response, the annotation overlay margins were revised to exclude benign connective tissue in non-background labels.
Corresponding patient reports and supporting immunohistochemical stains further guided annotation reviews. The microscopic diagnoses given by the primary pathologist in these reports detail the pathological findings within each tissue site, but not within each specific slide. The microscopic diagnoses informed revisions specifically targeting annotated regions classified as cancerous, ensuring that the labels “indc” and “dcis” were used only in situations where a micropathologist diagnosed it as such. Further differentiation of cancerous and precancerous labels, as well as the location of their focus on a slide, could be accomplished with supplemental immunohistochemically (IHC) stained slides. When distinguishing whether a focus is a nonneoplastic feature versus a cancerous growth, pathologists employ antigen targeting stains to the tissue in question to confirm the diagnosis. For example, a nonneoplastic feature of usual ductal hyperplasia will display diffuse staining for cytokeratin 5 (CK5) and no diffuse staining for estrogen receptor (ER), while a cancerous growth of ductal carcinoma in situ will have negative or focally positive staining for CK5 and diffuse staining for ER [9]. Many tissue samples contain cancerous and non-cancerous features with morphological overlaps that cause variability between annotators. The informative fields IHC slides provide could play an integral role in machine model pathology diagnostics.
Following the revisions made on all the annotations, a second experiment was run using ResNet18. Compared to the pilot study, an increase of model prediction accuracy was seen for the labels indc, infl, nneo, norm, and null. This increase is correlated with an increase in annotated area and annotation accuracy. Model performance in identifying the suspicious label decreased by 25% due to the decrease of 57% in the total annotated area described by this label. A summary of the model performance is given in Table 4, which shows the new prediction accuracy and the absolute change in error rate compared to Table 3.
The breast tissue subset we are developing includes 3,505 annotated breast pathology slides from 296 patients. The average size of a scanned SVS file is 363 MB. The annotations are stored in an XML format. A CSV version of the annotation file is also available which provides a flat, or simple, annotation that is easy for machine learning researchers to access and interface to their systems. Each patient is identified by an anonymized medical reference number. Within each patient’s directory, one or more sessions are identified, also anonymized to the first of the month in which the sample was taken. These sessions are broken into groupings of tissue taken on that date (in this case, breast tissue). A deidentified patient report stored as a flat text file is also available. Within these slides there are a total of 16,971 total annotated regions with an average of 4.84 annotations per slide. Among those annotations, 8,035 are non-cancerous (normal, background, null, and artifact,) 6,222 are carcinogenic signs (inflammation, nonneoplastic and suspicious,) and 2,714 are cancerous labels (ductal carcinoma in situ and invasive ductal carcinoma in situ.)
The individual patients are split up into three sets: train, development, and evaluation. Of the 74 cancerous patients, 20 were allotted for both the development and evaluation sets, while the remain 34 were allotted for train. The remaining 222 patients were split up to preserve the overall distribution of labels within the corpus. This was done in hope of creating control sets for comparable studies. Overall, the development and evaluation sets each have 80 patients, while the training set has 136 patients.
In a related component of this project, slides from the Fox Chase Cancer Center (FCCC) Biosample Repository (https://www.foxchase.org/research/facilities/genetic-research-facilities/biosample-repository -facility) are being digitized in addition to slides provided by Temple University Hospital. This data includes 18 different types of tissue including approximately 38.5% urinary tissue and 16.5% gynecological tissue. These slides and the metadata provided with them are already anonymized and include diagnoses in a spreadsheet with sample and patient ID. We plan to release over 13,000 unannotated slides from the FCCC Corpus simultaneously with v1.0.0 of TUDP. Details of this release will also be discussed in this poster.
Few digitally annotated databases of pathology samples like TUDP exist due to the extensive data collection and processing required. The breast corpus subset should be released by November 2021. By December 2021 we should also release the unannotated FCCC data. We are currently annotating urinary tract data as well. We expect to release about 5,600 processed TUH slides in this subset. We have an additional 53,000 unprocessed TUH slides digitized. Corpora of this size will stimulate the development of a new generation of deep learning technology. In clinical settings where resources are limited, an assistive diagnoses model could support pathologists’ workload and even help prioritize suspected cancerous cases.
ACKNOWLEDGMENTS
This material is supported by the National Science Foundation under grants nos. CNS-1726188 and 1925494. Any opinions, findings, and conclusions or recommendations expressed in this material are those of the author(s) and do not necessarily reflect the views of the National Science Foundation.
REFERENCES
[1] N. Shawki et al., “The Temple University Digital Pathology Corpus,” in Signal Processing in Medicine and Biology: Emerging Trends in Research and Applications, 1st ed., I. Obeid, I. Selesnick, and J. Picone, Eds. New York City, New York, USA: Springer, 2020, pp. 67 104. https://www.springer.com/gp/book/9783030368432.
[2] J. Picone, T. Farkas, I. Obeid, and Y. Persidsky, “MRI: High Performance Digital Pathology Using Big Data and Machine Learning.” Major Research Instrumentation (MRI), Division of Computer and Network Systems, Award No. 1726188, January 1, 2018 – December 31, 2021. https://www. isip.piconepress.com/projects/nsf_dpath/.
[3] A. Gulati et al., “Conformer: Convolution-augmented Transformer for Speech Recognition,” in Proceedings of the Annual Conference of the International Speech Communication Association (INTERSPEECH), 2020, pp. 5036-5040. https://doi.org/10.21437/interspeech.2020-3015.
[4] C.-J. Wu et al., “Machine Learning at Facebook: Understanding Inference at the Edge,” in Proceedings of the IEEE International Symposium on High Performance Computer Architecture (HPCA), 2019, pp. 331–344. https://ieeexplore.ieee.org/document/8675201.
[5] I. Caswell and B. Liang, “Recent Advances in Google Translate,” Google AI Blog: The latest from Google Research, 2020. [Online]. Available: https://ai.googleblog.com/2020/06/recent-advances-in-google-translate.html. [Accessed: 01-Aug-2021].
[6] V. Khalkhali, N. Shawki, V. Shah, M. Golmohammadi, I. Obeid, and J. Picone, “Low Latency Real-Time Seizure Detection Using Transfer Deep Learning,” in Proceedings of the IEEE Signal Processing in Medicine and Biology Symposium (SPMB), 2021, pp. 1 7. https://www.isip. piconepress.com/publications/conference_proceedings/2021/ieee_spmb/eeg_transfer_learning/.
[7] J. Picone, T. Farkas, I. Obeid, and Y. Persidsky, “MRI: High Performance Digital Pathology Using Big Data and Machine Learning,” Philadelphia, Pennsylvania, USA, 2020. https://www.isip.piconepress.com/publications/reports/2020/nsf/mri_dpath/.
[8] I. Hunt, S. Husain, J. Simons, I. Obeid, and J. Picone, “Recent Advances in the Temple University Digital Pathology Corpus,” in Proceedings of the IEEE Signal Processing in Medicine and Biology Symposium (SPMB), 2019, pp. 1–4. https://ieeexplore.ieee.org/document/9037859.
[9] A. P. Martinez, C. Cohen, K. Z. Hanley, and X. (Bill) Li, “Estrogen Receptor and Cytokeratin 5 Are Reliable Markers to Separate Usual Ductal Hyperplasia From Atypical Ductal Hyperplasia and Low-Grade Ductal Carcinoma In Situ,” Arch. Pathol. Lab. Med., vol. 140, no. 7, pp. 686–689, Apr. 2016. https://doi.org/10.5858/arpa.2015-0238-OA.
Winter, J.S.(
, Telecommunications Policy Research Conference (TPRC).)
Patient-generated health data (PGHD), created and captured from patients via wearable devices and mobile apps, are proliferating outside of clinical settings. Examples include sleep tracking, fitness trackers, continuous glucose monitors, and RFID-enabled implants, with many additional biometric or health surveillance applications in development or envisioned. These data are included in growing stockpiles of personal health data being mined for insight via big data analytics and artificial intelligence/deep learning technologies. Governing these data resources to facilitate patient care and health research while preserving individual privacy and autonomy will be challenging, as PGHD are the least regulated domains of digitalized personal health data (U.S. Department of Health and Human Services, 2018). When patients themselves collect digitalized PGHD using “apps” provided by technology firms, these data fall outside of conventional health data regulation, such as HIPAA. Instead, PGHD are maintained primarily on the information technology infrastructure of vendors, and data are governed under the IT firm’s own privacy policies and within the firm’s intellectual property rights. Dominant narratives position these highly personal data as valuable resources to transform healthcare, stimulate innovation in medical research, and engage individuals in their health and healthcare. However, ensuring privacy, security, and equity of benefits from PGHD will be challenging. PGHD can be aggregated and, despite putative “deidentification,” be linked with other health, economic, and social data for predictive analytics. As large tech companies enter the healthcare sector (e.g., Google Health is partnering with Ascension Health to analyze the PHI of millions of people across 21 U.S. states), the lack of harmonization between regulatory regimes may render existing safeguards to preserve patient privacy and control over their PHI ineffective. While healthcare providers are bound to adhere to health privacy laws, Big Tech comes under more relaxed regulatory regimes that will facilitate monetizing PGHD. We explore three existing data protection regimes relevant to PGHD in the United States that are currently in tension with one another: federal and state health-sector laws, data use and reuse for research and innovation, and industry self-regulation by large tech companies We then identify three types of structures (organizational, regulatory, technological/algorithmic), which synergistically could help enact needed regulatory oversight while limiting the friction and economic costs of regulation. This analysis provides a starting point for further discussions and negotiations among stakeholders and regulators to do so.
Winter, J.S.; Davidson, E.(
, Telecommunications Policy Research Conference)
Patient-generated health data (PGHD), created and captured from patients via wearable devices and mobile apps, are proliferating outside of clinical settings. Examples include sleep tracking, fitness trackers, continuous glucose monitors, and RFID-enabled implants, with many additional biometric or health surveillance applications in development or envisioned. These data are included in growing stockpiles of personal health data being mined for insight via big data analytics and artificial intelligence/deep learning technologies. Governing these data resources to facilitate patient care and health research while preserving individual privacy and autonomy will be challenging, as PGHD are the least regulated domains of digitalized personal health data (U.S. Department of Health and Human Services, 2018). When patients themselves collect digitalized PGHD using “apps” provided by technology firms, these data fall outside of conventional health data regulation, such as HIPAA. Instead, PGHD are maintained primarily on the information technology infrastructure of vendors, and data are governed under the IT firm’s own privacy policies and within the firm’s intellectual property rights. Dominant narratives position these highly personal data as valuable resources to transform healthcare, stimulate innovation in medical research, and engage individuals in their health and healthcare. However, ensuring privacy, security, and equity of benefits from PGHD will be challenging. PGHD can be aggregated and, despite putative “deidentification,” be linked with other health, economic, and social data for predictive analytics. As large tech companies enter the healthcare sector (e.g., Google Health is partnering with Ascension Health to analyze the PHI of millions of people across 21 U.S. states), the lack of harmonization between regulatory regimes may render existing safeguards to preserve patient privacy and control over their PHI ineffective. While healthcare providers are bound to adhere to health privacy laws, Big Tech comes under more relaxed regulatory regimes that will facilitate monetizing PGHD. We explore three existing data protection regimes relevant to PGHD in the United States that are currently in tension with one another: federal and state health-sector laws, data use and reuse for research and innovation, and industry self-regulation by large tech companies We then identify three types of structures (organizational, regulatory, technological/algorithmic), which synergistically could help enact needed regulatory oversight while limiting the friction and economic costs of regulation. This analysis provides a starting point for further discussions and negotiations among stakeholders and regulators to do so.
Sharko, Marianne, Wilcox, Lauren, Hong, Matthew K, and Ancker, Jessica S. The Variation in Patient Portal Access for Adolescents in the United States: How Different Medical Centers Manage their Adolescent Access. Retrieved from https://par.nsf.gov/biblio/10084799. American Medical Informatics Association (AMIA) 2017 Annual Symposium .
Sharko, Marianne, Wilcox, Lauren, Hong, Matthew K, & Ancker, Jessica S. The Variation in Patient Portal Access for Adolescents in the United States: How Different Medical Centers Manage their Adolescent Access. American Medical Informatics Association (AMIA) 2017 Annual Symposium, (). Retrieved from https://par.nsf.gov/biblio/10084799.
Sharko, Marianne, Wilcox, Lauren, Hong, Matthew K, and Ancker, Jessica S.
"The Variation in Patient Portal Access for Adolescents in the United States: How Different Medical Centers Manage their Adolescent Access". American Medical Informatics Association (AMIA) 2017 Annual Symposium (). Country unknown/Code not available. https://par.nsf.gov/biblio/10084799.
@article{osti_10084799,
place = {Country unknown/Code not available},
title = {The Variation in Patient Portal Access for Adolescents in the United States: How Different Medical Centers Manage their Adolescent Access},
url = {https://par.nsf.gov/biblio/10084799},
abstractNote = {Adolescence is a time when patients are approaching autonomy, both developmentally and legally. Yet they are still minors and are likely to encounter contradictions between situations in which they are treated as children and ones in which they are treated as adults. Being able to access their medical information may enable adolescents to take on a participatory role in their health care. However, federal policy, state law, and community norms are not consistent regarding adolescent healthcare and privacy. For example, in some regions and under some circumstances, adolescents may have consent and privacy rights similar to those of adults, with the right to make some, or all, of their own sensitive medical decisions privately. In other cases, parental notification is the norm, or guidance is unclear or lacking. In the absence of national guidelines, medical centers encounter serious challenges when developing policies about adolescent access to medical records via patient portals. The American Academy of Pediatrics has made recommendations, but these are not binding. To explore diversity in adolescent privacy policies and identify common approaches, we are conducting a qualitative study with key informants from different types of medical organizations in different regions of the country. The main objective is to identify diversity in adolescent privacy features within the patient portal. Another objective is to enumerate the factors involved in making portal access decisions. A third objective is to identify the potential need for more formalized guidance and standards on privacy features within the patient portal},
journal = {American Medical Informatics Association (AMIA) 2017 Annual Symposium},
author = {Sharko, Marianne and Wilcox, Lauren and Hong, Matthew K and Ancker, Jessica S},
}
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