HIMAP Project Maps Health Data Flow
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The Indiana University Center for Law, Ethics, and Applied Research in Health Information (IU CLEAR) aims to enhance the ethical and lawful use of health information through collaboration with healthcare providers, patients, and other stakeholders. IU CLEAR seeks to develop a more rational approach to using personal health data from various digital sources for research while respecting patient privacy. The Health Information Map (HIMAP) project aims to map and understand the holistic view of health information generated from various sources on a daily basis to better facilitate treatment, research, and accountability.
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HIMAP Project Maps Health Data Flow
- 1. HIMAP Health Information Map IU CLEAR Project Kristin Eilenberg, Project Leader 1 @keilenberg #HIMAP #healthapps
- 2. School of Law & Ethics School of School of Informatics Medicine The Indiana University Center for Law, Ethics, and Applied Research in Health Information’s purpose is: • To enhance the ethical, lawful, and practical use of health information to facilitate treatment and research, improve health outcomes for patient, and facilitate accountability. • To work with key constituencies including healthcare providers and payers, patients, ethicists, attorneys, and professional groups, regulators, and others to devise a more rational and more trustworthy approach to using personal data for health research, one that recognizes that more and more relevant data comes from the internet and other digital sources that are largely beyond the scope of the current health privacy laws. 2 @keilenberg #HIMAP #healthapps
- 3. What is Health 2.0? [video] 3 @keilenberg #HIMAP #healthapps Ref: YouTube video - Health 2.0 response to The Machine is Us/ing Us
- 4. 4 @keilenberg #HIMAP #healthapps Ref: Geographies of the World’s Knowledge, Convoco 2011
- 8. YouTube downloads 48 hours of video every 1 minute and more than 3 billion views/day 8 @keilenberg #HIMAP #healthapps
- 9. 8 in 10 internet users look online for health information Ref: Pew Research Center’s Internet & American Life Project and the 9 @keilenberg #HIMAP #healthapps California HealthCare Foundation, Feb 2011
- 10. Who is driving the health discussions? 10 @keilenberg #HIMAP #healthapps Ref: NMIncite, 09/2011
- 13. Project Overview 13 @keilenberg #HIMAP #healthapps
- 14. HIMAP Health Information Map • Purpose – Develop a Health Information Map that will represent the holistic and complicated view of all of the health information that is generated on a daily basis within computerized healthcare systems, the internet and social media platforms, and through consumer purchases of health related products and services – To better understand what health information is created, where it is created, and how it is used 14 @keilenberg #HIMAP #healthapps
- 15. Health Information Map • How – Mobilize and partner with experts in the medical profession, electronic health records, health information data transactions, internet search, social media platforms, patient communities, and mobile/tele- medicine systems – Develop an activity based model and mapping of the healthcare information/data that is generated on a daily basis related to disease: initial symptoms, diagnosis, treatment, survival/recovery, and re- occurrence – Document key content creators and users of the data, primary locations of where the data is generated, how the data is shared and merged with other data sources, who owns the data, how the data is stored, and the sensitivity or impact of the data 15 @keilenberg #HIMAP #healthapps
- 16. Assumptions 16 @keilenberg #HIMAP #healthapps
- 17. Patient Stages of Disease Symptom Recognition Diagnosis Treatment Initiated Treatment Ongoing Survival/Remission Maintenance Re-occurrence 17 @keilenberg #HIMAP #healthapps
- 18. Information Seeking, Use, and Creation Behaviors Purchase Seek Create Find content/Comment/Rate Share/Connect Use/Apply 18 @keilenberg #HIMAP #healthapps
- 19. E-Rx/Pharmacy/PBM Prior Analytics Co Insurance Alerts Authorization Status Pharma De-identified Data Sets Research Claim De-identified Adjucated Claim information Data Sets Health Claim Drinking Type Contact info Medications Allergies behaviors Prescriber ID Insurance Procedures Immunizations Smoking history Employment Diagnoses Hospitalizations Other health Analytics Co Claims history history Clearinghouse De-identified & Provider Biometric data Laboratory Genetic Data Sets seen/referred results Information Insurance Co Hospital Electronic Health Record Analytics Co De-identified Data Sets 19 @keilenberg #HIMAP #healthapps
- 20. Phone ID User, Password Health Geo- Preferences Age, Gender habits location and Interests Drinking behaviors User, Activities Contact Info Lab results Password Preferences Religious and beliefs Interests Networks Social IP Address Allergies Mobile Age, Gender Phone App Network Political Family Employment Activities views Medications Contact Info Networks Memberships Drinking Affiliations behaviors Biometric Procedures data Political views Religious beliefs Diagnoses User, Password User, Password Genetic info Age, Gender Genetic info Age, Gender Employment Lab results Contact Info Lab results Contact Info Preferences and Drinking Interests behaviors Hospitalization IP Address s Personal Hospitalizations IP Address Activities Online Religious beliefs Health Insurance Immunizations Health Insurance coverage Immunizations Network coverage Records Networks Political views Provider Allergies Provider seen/referred Allergies seen/referred Memberships Family Affiliations Medications Biometric data Medications Biometric data Procedures Diagnoses Procedures Diagnoses 20 @keilenberg #HIMAP #healthapps
- 21. Basic Framework Example User, Genetic Password Age, info Gender Contact Lab results Info Hospitaliza IP Address tions Personal Immunizati Health Insurance ons coverage Records Provider Allergies seen/refer red Medication Biometric s data Procedures Diagnoses Internet User, Password User, Genetic info Age, Gender Preferences Password Phone ID and Age, Gender Interests Lab results Employment Contact Info Health habits Geo-location Preferences Drinking and Drinking Activities Contact Info behaviors Interests behaviors User, Hospitalizati Lab results IP Address Password ons Online Religious Preference s and Mobile Religious beliefs Networks Social IP Address Activities Health beliefs Interests Network Immunizatio Insurance Allergies Phone Age, Gender Family Employment ns Network Political coverage App Networks views Political Provider Activities views Allergies seen/referre Medications Contact Info Membership d Membership Drinking Family s Affiliations behaviors s Affiliations Networks Biometric Biometric Political Religious Medications Procedures data data views beliefs Procedures Diagnoses Diagnoses 21 @keilenberg #HIMAP #healthapps
- 22. Proliferation Example User, Genetic Password Age, info Gender Contact Lab results Info Hospitaliza IP Address tions Personal Immunizati Health Insurance ons coverage Records Health habits Phone ID Geo-location User, Password Provider Genetic info Allergies Age, Gender seen/refer Drinking red behaviors Phone ID User, User, User, Employment Medication Lab results Preferences Password Lab results Contact Info Biometric Password Health habits Geo-location and Age, Gender Preferences Password Preferences s data Preference Drinking User, Religious and Age, Gender Interests Interests and Interests behaviors Procedures Diagnoses Genetic info Password s and Age, Gender Interests Mobile beliefs Phone ID Drinking behaviors User, Hospitalizati Lab results Activities Contact Info Activities onsContact Info Online IP Address Lab results Employment Allergies Contact Info Phone Health habits Age, Gender Geo-location Preference Password Religious Activities Religious beliefs Preferences and Drinking App Drinking s and behaviorsInterests Mobile beliefs Social Health Interests behaviors Lab results Political User, Password Networks Networks IP Address Social Immunizatio IP Address Insurance Hospitalizati Activities IP Address Preference Allergies views Phone Age, Gen Network ns Network coverage ons Online Medications s and Contact Info Mobile Religious beliefs User, Network Networks Genetic info User, PasswordPolitical views Gender Age, Activities Religious beliefs Networks Interests App Family Preferences Family Interests Password and Employment Age, Gender Allergies Employment Provider seen/referre Immunizatio Health User, Procedures Allergies Health habits Insurance Phone ID Biometric Phone data Geo-location Medications Activities Age, Gender Political views Contact Info Employment d ns Activities Contact Info Lab results Family Membership PreferencesAffiliations and s Drinking Contact Info Network Genetic info Password Age, Gender Political coverage Diagnoses Drinking behaviors App Health habits Phone ID PoliticalNetworks Geo-location Membership Drinking Biometric behaviors Networks Activities User, s Affiliations Membership behaviors Drinking Medications Interests data views Lab results views Biometric Employment Provider Medications Procedures Password Contact Info Drinking s Affiliations behaviors Lab results Contact Info data Political Religious Hospitalizati AllergiesAddressPreferences Preference seen/referre behaviors Procedures Diagnoses IP Drinking Religious Diagnoses User, views beliefs Networks Social Political views Religious beliefs IP Address ons Activities Online Religious beliefs and InterestsFamily Membership behaviors s Affiliations d s and Interests Mobile Lab results beliefs Networks Preference Biometric Religious Password Procedures Network Immunizatio Health Hospitalizati Medications ons Insurance Online Biometric Allergies data IP Address Phone s anddata Age, Gender Diagnoses Interests Mobile beliefs Religious Family Employment ns Network Political coverage Activities Procedures Health Diagnoses beliefs App Allergies Political Phone Age, Ge Networks Activities Allergies Immunizatio views ns Provider seen/referre Network Insurance Medicationscoverage views Contact Info App Political Membership Drinking d Activities Membership Political views s Affiliations behaviors Family Networks Networks Medications Contact Info s Affiliations views Political Religious Biometric Allergies Provider Procedures seen/referre Biometric data 22 @keilenberg #HIMAP #healthapps views beliefs Medications Procedures Diagnoses data Family Membership s Affiliations d Diagnoses Procedures Networks Biometric data
- 23. E-Rx/Pharmacy/PBM Prior Analytics Co Insurance Alerts Authorization Status Pharma De-identified Data Sets Research Claim De-identified Adjucated Claim information Data Sets Health Claim Drinking Type Contact info Medications Allergies behaviors Prescriber ID Insurance Procedures Immunizations Smoking history Employment Diagnoses Hospitalizations Other health Analytics Co Claims history history Clearinghouse De-identified & Provider Biometric data Laboratory Genetic Data Sets seen/referred results Information Insurance Co Hospital Electronic Health Record Analytics Co De-identified Data Sets 23 @keilenberg #HIMAP #healthapps
Notas del editor
- Initial prototype was built to represent the raw data that was captured per Agent. Each box on the grid represents a single attribute. In the live prototype, when the cursor is hoovering over the box, a fly over window appears and shows the name of the attribute and the data value for that Agent.This represents the data that is captured by Gmail. The darker squares are ‘required’ fields. The lighter shade is data that is discretionary for the end user to decide what they will provide.
- What this slide means:The YND score represents the amount of data attributes that are collected by an Agent. Each attribute that is required/absolute scores 2; each attribute that is discretionary and allows the user to decide if they are going to provide the information/content scores 1; each attribute that is not collected scores 0.The Sensitivity score represents the sum of attributes that an Agent collects. Each attribute has been assigned a ‘sensitivity’ rating: 1 = not sensitive. Even if the data attribute was revealed/released/known to others it would not cause any harm to the person whether in the form of discrimination, embarrassment etc. 2 = somewhere between 1 and 3... not an ideal definition, but we are working on getting this clarified.3 = very sensitive. If the data attribute were revealed/released/known to others it could cause harm (or very likely cause harm) to the individual whether directly or indirectly.The size of the orbs represent the sum of each attribute’s YND rating multiplied by the sensitivity ranking. Interpretation of the data – EHRs capture sensitive information, but most elements are considered discretionary. In comparison, there are many internet websites that capture almost as much information as EHRs, yet have higher scores on the Sensitivity axis.
- http://www.mappinghealth.com/iuclear/