Hello, I'm Marc Beller and I'm here to talk to you about information systems used in health care. as you might expect, there are many informations used, information systems used in health care and there are many data produced by the use of these systems. These data can be rich for use in research. I chose to categorize the information systems in this fashion. We have information systems that are used for general administration, for research administration, clinical operations, clinical care, there are ancillary clinical systems, departmental systems, and data aggregation systems. This drawing is intended to depict many of the information systems that are commonly used in health care organized by the category. You'll notice that one of the systems, the electronic health records system, actually appears in both categories because this system is used for direct clinical care as well as it is considered an aggregation system. Some of the general administration systems you might encounter are the financial systems like the general ledger which is responsible for accounting functions such as budget, expenses, financial reporting. Also, there is the procurement and disbursement system used to track accounts payable, accounts receivable with vendors. The human resources system is responsible for knowing who the people are in the organization and payroll, very important, and the time and attendance system is used to track just that, time and attendance for hourly staff. Contract systems are used to manage legal agreements, such as expirations, and reminders. And then incident management systems are used by help desks to record customer inquiries and technical issues. In research we have many administrative systems. A clinical trials management system may be used for tracking the research studies, the protocols, the study contacts and the study participants. The institutional review board or IRB system is used for tracking research studies, protocols, consent forms. Grant management is used to spo, to track study sponsor information. And data management systems are used to collect information and for use in reporting. You also might have a bio-specimen management system where tissue samples can be used stored and tracked for research. So a question, where would data be obtained to asses help desk call volume? And the answer, would be, in our incident management system, if your organization employs one. Clinical operation systems at a, at their core include, perhaps, a hospital admitting and registration and billing system where we track patient stays, insurance billing, what we refer to sometimes as technical charges, and room and bed charges. The physician scheduling and billing system tracks patients clinic visits, and also does billing for physician type of services, which we refer to as professional. There also may be a, a separate or integrated bed management system, which tracks patients location within the facility. Additional clinical operation systems includes staff scheduling, which is used often by nursing. A patient transport and housekeeping services systems, which track tasks that are dispatched and then tracked to completion. A nutrition management system where we track patient meals and their distribution. And a risk management system for self reporting of incidents and tracking resolution. So, another question, where would we obtain data to assess hospital admissions? And the answer would be in our core hospital admitting registration and billing system. Clinical care systems, at their heart, would include a medical record or an electronic health record, this is also referred to as a patient's chart. This information would include allergies, immunizations, diagnosis, medications, vital sights, procedures, lab test results, interpretive reports, care plans and many other pieces of information. In addition to an electronic health record, you might have a prescription writer which is used to write prescriptions for patients to take home and fill at their local pharmacy. In addition, you might have in patient and outpatient order entry systems, which allows providers to order procedures, lab tests or medications for their patients. There are documentation systems, some used by nursing, some used by physicians. And there are biomedical systems, which include devices that are connected to the patient and produce data output, which is, which can then be used. Ancillary clinical systems include pharmacy, laboratory, radiology and several others. These systems tend to track orders and filling of those orders for medications or diagnostic testing. And the radiology systems typically include a procedure tracking module as well as an imaging module. Now I should point out that the imaging module may be generalized for use across radiology and other services like cardiology or they may be dedicated for individual applications. In addition, you might have a materials management system, which would track inventory, consumption and replenishment of those items. And we have departmental systems, which would include things like an emergency department system for tracking patients and documentation. operative services systems, which would be used in the OR for scheduling procedures and tracking those procedures and documenting them. And there might be a medical transport system used for air and ground transport of patients to the facility or between facilities, these sometimes also include a documentation component. So, some more questions. Where would we obtain data to assess inpatient medication dispensing? The keyword there is dispensing. We would obtain that from the pharmacy system where we track the medication orders and the inventory as well as dispensing. Where would we obtain data to assess ER wait times? That would be in our Emergency Department patient tracking system, which typically has a waiting room and then the time between waiting and patient being put in a particular room in the Emergency Department. In addition, and finally, we have data aggregation systems. These are systems that collect data from other source systems, such as, as I previously mentioned, the electronic health record, a personal health record, which is typically a patient-facing view this could also be re, referred to as a patient portal. There are enterprise data warehouses that aggregate data from a variety of systems. And then there are research oriented, what I would call data warehouses or data marts, such as patient identifiable data sources as well as de-identified data sources. Now there are some challenges to think about, specifically with data aggregation systems, there's magic that happens in between the source systems and the aggregation systems. One of the potential issues is that not all of the data is integrated from a particular system or the, not all of the data sources are pro, are provided in the enterprise data warehouse. In, in addition you have to consider the pros and cons of each each potential solution. So for example, obtaining data from the source systems, this data are not integrated with other data and that integration would have to be performed on your own. In addition, there could be some production impact in obtaining data from the source system such as response time based on system resources when you're running such a query. The alternative, ob, obtaining data from the aggregation systems, the content may or may not be complete, again as I mentioned, not all systems and not all the data. There also can be time delays in getting the data from the source systems to the aggregation systems which may or may not be an issue for your purposes but nightly updates are very typical in this environment. Other potential challenges would include duplicate patient medical record numbers where a patient, the same patient has multiple medical records and could be there for counted multiple times. this is a common problem in health care that is consistently being addressed and hopefully the small volume of this occurrence would be negligible in your research. In addition free text data is often available in the medical record, but it's difficult to query because it's not discreet data and therefore, there's some techniques like natural language processing, which can be used to remedy that. In addition, there can be different nomenclature used across systems that could make things challenging. Billing diagnosis codes, they can be used to rule out a diagnosis and therefore, can not necessarily be used to confirm a patient's diagnosis. Prescriptions, they are written but they may or may not get filled by the patient and even if they do get filled by the patient, we def, definitely don't know whether or not they have taken them. And patient location information can be incomplete or inaccurate. there are real-time location systems using RFID or other technology that are improving on our ability to be able to know where a patient was in our facility. And, finally there can be duplicate systems used by different departments. For example the adult versus pediatric areas of a hospital may have different systems to track cardiology procedures and, and imaging and that sort of thing. So, there might be some challenges there in obtaining a set of data from multiple systems that should be the same but would potentially be different based on different vendors being used. with imaging data, imaging files can be quite large in size and that takes a while to transfer, and the meta data are usually available in the envelope that the imaging files are contained, but this can be limited and may or may not meet your needs. And, finally and importantly, de-identification of image data can be difficult because often there can be patient identifiable information within the image itself. So, in closing there are many information systems used to support health care. The data produced by these systems is rich for research and knowing where to find data to answer research questions will help you on your way. Thank you.