Copy of Appendix A - Supplemental Data Resource Table Final_.xlsx
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Sheet1
| Sentinel Initiative: Request for Information |
| November 2022 | |
APPENDIX A
Data Resource Information Table
| Data Resource | Brief description |
| Use the shortest description possible to convey the information. | |
| What data sources would you use to cover 100 million lives in healthcare administration claims and 10 million lives in linked electronic Health Record (EHR) data? Describe. | |
| Population size/characteristics by each proposed data source | |
| Patients currently and cumulatively enrolled (n) | |
| Person time by calendar year | |
| Demographic distribution and geographic diversity of patients | |
| Representation of special populations | |
| Demographic comparison of the database population to the larger geographic population | (e.g., state-specific, region-specific, or country-level census data) |
| Rate of patient continuance within database | |
| Description of patients, services, or pharmaceutical products that might not be included in the database | (e.g. elderly or indigent populations; elective procedures; psychiatric or prenatal care or services; acute or chronic conditions treated or services provided outside the health plan; drug or biologic products whose administration, prescription or purchase are not captured in the database; smoking cessation clinic visits; etc.) |
| Total number and validated number of years of data available | Present in table format |
| Overall mean, median and range of observation time for active patients in the database | |
| How many patients had at least one visit last year? | |
| Lag time between date of service and data availability | |
| Month and year data collection began | |
| Availability of a complete data dictionary (including percentage of missing data) | |
| Describe the extent of missing data over time (i.e., the percentage of data not available for a particular variable of interest) and discuss procedures (e.g., exclusion, imputation) employed to handle this issue | |
| Method and frequency of refreshing/updating each proposed database | |
| What is the number of mother-infant linked records? | |
| Geographic distribution of participants | |
| National (US)? | |
| Nationally representative of US? | |
| Patient demographics | |
| Age | Specify method by which age is obtained |
| Gender/Sex | |
| Race and ethnicity | Specify method by which race/ethnicity are obtained |
| For what proportion of patients is race and ethnicity captured? | |
| Unique encrypted patient ID that is constant over time | |
| Patient-specific drug or biologic therapy information | |
| Generic/established name of drug/medical product | |
| Strength | |
| Product form | |
| Dispensed dose | |
| Prescribed dose | |
| Instructions for use | |
| Dates of all dispensed prescriptions | |
| Ability to determine absolute initial versus continuing therapy | Include description of ascertainment of this information |
| Days supply | |
| Prescriber specialty | |
| Anonymized unique prescriber identifier | |
| Quantity dispensed | |
| Indication for drug/medical product therapy | |
| Nonprescription drug product use | |
| Setting of care | |
| Ability to verify that the prescription was dispensed to the patient, rather than merely prescribed. | |
| Serum levels for drug and date measured | |
| Route of administration | |
| Type of coding system(s) used for drugs and medical products (e.g., NDC, RxNorm) | |
| Patient medical diagnostic information | |
| Diagnoses and procedures associated with all ambulatory visits and their dates | |
| Diagnoses and procedures associated with all hospitalizations and their dates | |
| Laboratory tests and dates | |
| Laboratory tests results and their dates | |
| Tobacco use and smoking status | |
| BMI, height and weight | |
| Alcohol use | |
| Cannabis use, duration, quantity per day | |
| Non-medical use of FDA-approved drug use | |
| Blood pressure measurements | |
| Heart rate at time of blood pressure measurements | |
| Medical, past history | |
| Family health history | |
| Description of process for identifying pregnancies and pregnancy outcome after birth | |
| Ability to link maternal with child records and vice versa | |
| Spontaneous abortions, miscarriages, other pregnancy outcomes | |
| Cancer diagnoses | |
| Cancer diagnoses with links to cancer registries | (validated staging, please append list of diagnoses) |
| Chronic disease registries linkages | (please append list) |
| Pathology reports and dates | |
| Patients with linked DNA biobank data | |
| Vital statistics data linkages, including | |
| National Death Index linkage | |
| Death certificates | |
| Birth certificates | |
| EHR Source | |
| How many patient lives are represented in your EHR data source? | |
| How many patients contribute both EHR data and claims data to your data source? | |
| What is your approach for linking claims to EHR data? | |
| What tools, methods were used? | |
| How many pediatric patients contribute both EHR and claims data to your data source(s)? | |
| How does this compare to the number in two years prior? | |
| What are your expectations for how this will compare with the number two years from now? | |
| For patients with both EHR and claims data, how much of a patient’s observation time is overlapping between both EHR and claims data, on average? | |
| On average, how many visits or encounters (e.g., hospitalization, office visit, lab draw) does a patient have in your EHR data source per year? | |
| How many patients have at least one visit in the EHR data in each of the last three years? | |
| Are the following data elements available from your EHR data for analysis? | |
| Coded data using standard terminologies | |
| Extracted clinical concepts from unstructures EHR notes (e.g., using Natural Language Processing technique) | |
| Clinical Notes | |
| Free text clinical note search (ability to search chart notes for a large cohort of patients simultaneously) | |
| Prescription administration/dispensing information | |
| Lab procedures (test occurred and/or administrative codes) | |
| Lab values (microbiology, chemistries, hematology, inflammatory markers, pregnancy tests, viral respiratory panels) | |
| Lab values (microbiology, chemistries, hematology, inflammatory markers, pregnancy tests, viral respiratory panels) | |
| Vital signs | |
| Death data | |
| Imaging procedures (test occurred and/or administrative codes) | |
| Imaging reports | |
| Pathology procedures (test occurred and/or administrative codes) | |
| Pathology reports (biopsies, tumor grade) | |
| Radiology procedures (test occurred and/or administrative codes) | |
| Radiology reports (pregnancy related ultrasounds, head MRI) | |
| Demographic characteristics | |
| Current diagnoses | |
| Medical history | |
| Allergy diagnoses and treatment | |
| Immunizations history | |
| Provider order entry records (orders) | |
| Procedure notes (surgical procedure notes) | |
| Intraoperative exposures (anesthesia) | |
| Dialysis | |
| Is the time period available different for different locations or providers? | |
| Care settings that are covered: | |
| Inpatient | |
| Outpatient/ambulatory | |
| Telehealth services (health-related services and information via electronic information and telecommunication technologies) | |
| Emergency department | |
| Skilled nursing facility or palliative care | |
| Oncology | |
| Thinking about EHR data that are available for secondary use, how frequently are the research data updated? | |
| Do refreshes vary by the specific field? | |
| What terminologies are available in your EHR data source? | |
| NDC | |
| RXNORM | |
| ICD-9 | |
| ICD-10 | |
| ICD-11 | |
| HCPCS/CPT | |
| ISBT-128 (blood products) | |
| Codabar (blood products) | |
| LOINC | |
| NUCC | |
| SNOMED-CT | |
| Currently available tools or established routine capabilities to address the following | |
| Drug utilization inquiry | |
| Utilization patterns across multiple products | |
| Background rates for adverse events/outcomes | |
| Incidence rates | |
| Propensity score analysis | |
| Inverse Probability Treatment Weighting (IPTW) | |
| Signal identification | |
| Describe the extent that artificial intelligence (AI) tools such as machine learning (ML) and natural language processing (NLP) are used by organization to interrogate your data. | |
| Describe the extent and breadth of any already established and operational Natural Language Processing Tools | |
| Describe the extent and breadth of any outcome codes already validated in these resources (include sensitivity and specificity) | |
| Do you use data standards (e.g., Fast Healthcare Interoperability Resources (FHIR)) in the EHRs or to transfer data to medical claims? | |
| If so, how is it used or how do you project it to be used to support FDA work? | |
| Can medical records be obtained for a portion of your data? | |
| If so, from what source and in what proportion? | |
| If not, what other approaches support more in-depth understanding of the patient's profile and for what proportion? | |
| Other | |
| What is the time required for contracting with your organization? | |
| Characterize the data quality-control processes used and quality control process of any mapped or transformed data. | |
| Could you provide examples of a published analyses that illustrates your technical capability, capacity, and the strengths of your data sources? | |
| What capabilities and advantages do your data sources have that differentiate your capabilities to meet the Sentinel system needs as opposed to other data sources? | |
| Describe how you will protect patient privacy and Protected Health Information (PHI) (HIPAA compliance) | |
| Describe the frequency and type of any data error corrections or changes in data adjudication policies implemented | |
| Describe the general procedures used to ensure completeness, consistency, and accuracy of data collection and management |
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