Use+Case+1+Local+Data+Access+User+Stories

include component="page" wikiName="siframework" page="daf header" =Sample Local Data Access User Stories=
 * Please Note: The draft user story document below has been revised and is current as of Wednesday, September 18th, 2013. Community members have reviewed and voted on these user stories. **

This page includes a sampling of real world business scenarios and user stories for Local Data Access. =Local Data Access User Stories= A word document of the drafted user stories, is attached for anyone interested in revising/updating sample User Stories.

// ** Please note this User Story Document has been revised based on the Wednesday September 18th, 2013 Local DAF All Hands Call. ** //

Document Metadata based access (Selected user story for Local DAF Use Case)

Patient Level Query #1


 * A Provider accesses clinical summary documents on an ad hoc basis for a new diabetic patient with documented poor glucose control **

A new patient arrives to a small family practice in Boston, MA. The PCP sees a 48 year-old male, with Diabetes Mellitus Type I (DM I) diagnosis since age 12. The patient has a history of myocardial infarction (MI) at age 37 and a stroke at age 43. The patient admits that he often forgets to take his medication as prescribed and often forgets to check his blood sugar levels throughout the day. The patient travels for works and has been admitted to different ER’s numerous times for acute complications due to elevated blood sugar levels. All healthcare facilities where the patient was admitted generated clinical summaries and sent the information to patient’s new physician at the patient’s request. The clinical summaries have been stored in the local document repository database within the organization. **//For today’s visit, the physician generates an ad-hoc query within the EHR to access all clinical summary documents produced locally and those received from other healthcare facilities, so that he can check if the patient’s HbA1c levels were greater than 7% and if the glucose levels were greater than 100mg/dL over the past 5 years the EHR system queries the document repository database to retrieve the requested information and sends back multiple clinical summary documents to the physician for additional review.//** This information provides the physician required context to understand the severity of circumstances that led to the patient’s ER admission, the severity of the patient’s non-adherence to medications and formulate a plan to improve the patient’s lifestyle and adherence to medications to mitigate future ER visits and reduce or prevent the progression of established comorbidities.

Data Element based access (Selected User Story for Local DAF Use Case)

Patient Level Query #1


 * Physician referral to Endocrinologist within the same organization using different EHRs with system alerts for patient protected information **

In accordance with best practice, the Gastroenterologist orders fasting glucose lab tests for new or current Hepatitis C patients. **//The Gastroenterologist’s EHR receives results from source systems based on queries//** which are set up to run automatically,and alerts him when a patient’s fasting glucose lab results are between 100 mg/dL and 125 mg/dL. During an initial encounter with a VA patient for Hep-C, the Gastroenterologist is alerted that the patient’s glucose intolerance lab results are very high. The Gastroenterologist wants to refer the patient to an Endocrinologist in his practice. **//In preparation for the referral, the Gastroenterologist queries the repository for all of the patient’s records including sensitive records disclosed to him by the VA per the patient’s consent. The Gastroenterologist receives a response to this query//** and is alerted that information related to the patient’s Hep-C, which was diagnosed during substance abuse treatment, is protected under Title 38, and may not be disclosed without patient consent. Before making the referral, the Gastroenterologist asks the patient whether she consents to disclose protected information to the Endocrinologist. The patient agrees, and signs an electronic consent directive. The Gastroenterologist’s EHR updates the security labels on this patient’s protected information authorizing the Endocrinologist to query for her records. **//When the Endocrinologist’s EHR system queries Gastroenterologist’s EHR, it is authorized to receive the patient’s records including the Title 38 protected information. When researchers within the Endocrinologist’s practice query for Hepatitis C patients, they will not receive the results for patients who have not consented to disclosure for research, because they are not authorized//.**

__Additional User Stories to Add to Local DAF Appendix __

Patient Level Query
 * Document Metadata based access **
 * A provider needs to access information for one of his patients’ who recently moved to a new state and that has a new care team. **

A patient is moving from Michigan to Florida for retirement. The patient has diabetes and has also undergone multiple open heart surgeries to correct irregular heartbeats and other ailments related to heart. His new care team in Florida is preparing for an initial visit and has requested the patient to retrieve his medical history from as many sources as possible. The patient approaches the Michigan hospital, PCP and the cardiologist office who are part of the current team and where he has received treatment before. He requests each one to provide his medical records (clinical documents) to date. The providers query each of their local EHR systems to obtain the clinical documents, requested by the patient. Now that the patient has all necessary records, he can carry them with him on his initial visit to a new care team in Florida.

Population level Query A primary care physician’s patient panel has a significant number of male patients who have cardiovascular disease and diabetes over the past 5 years. She wants to further analyze the clinical summaries of her male patient population over the past 5 years using a 3rd party analytical application external to the EHR System. She queries her EHR system to retrieve clinical office summary visit documentation for patients over the past 5 years. The results of the query are returned to her in a structured document format for each of the patients fitting those criteria. Once she receives the results, she further analyzes the summaries by using an external 3rd party analytical application to break down cohorts of those patients with mild, moderate, and severe disease to determine who are missing recommended preventive and disease management services such as lab checks and diabetic foot exams.
 * PCP searches for office visit summaries in local EHR system to further analyze them using 3rd party software system (external to EHR) to understand severity of illness in patient population **

<span style="font-family: Arial,sans-serif; font-size: 13pt; line-height: 1.5;">Patient Level Query <span style="font-family: Arial,sans-serif; font-size: 11pt;">A primary care physician’s patient panel has a significant number of male patients who have cardio vascular disease and diabetes over the past 5 years. She already has a list of male patients and their clinical office visit summary documents that she was able to retrieve through a previous query search in her EHR. She wants to use that list of patients now to drill down within each of these documents to identify patients with cardiovascular disease and diabetes over the past 5 years. The PCP sends one query to her EHR system for all identified patients to retrieve patients with diagnoses of cardiovascular disease and diabetes over the past 5 years. The query returns a list with associated documents that match the query request. Once she receives the results, she further analyzes the summaries by using an external 3rd party application to break down cohorts of those patients with mild, moderate, and severe disease to determine who is missing recommended preventive and disease management services such as lab checks and diabetic foot exams.
 * <span style="font-family: Arial,sans-serif; font-size: 14pt;">Data Element based access **
 * <span style="font-family: Arial,sans-serif; font-size: 11pt;"> PCP searches for office visit summaries in local EHR system to further analyze them using 3rd party software system (external to EHR) to understand severity of illness in patient population **

<span style="font-family: Arial,sans-serif; font-size: 13pt;">Patient Level Query <span style="font-family: Arial,sans-serif; font-size: 11pt;">A Primary Care Provider (PCP) at Virginia Family Medicine Center (VFMC) recently ordered an HbA1c test for a new patient with established Diabetes Type 1 diagnosis. The patient had been to VFMC several times before, but just recently switched her PCP internally at VFMC. The PCP received the test results for a specimen drawn on 7/5/2013 in her EHR system indicating that the patient’s HbA1c was 8.3%. Her PCP would like to determine her patient’s glucose level trend over the past 12 months. The PCP formulates a query in her EHR system to retrieve all HbA1c results where the patient’s levels were above 7% at VMFC. The PCP receives a single response of available results from one or more responding application(s) where this data was documented. The PCP is able to obtain all of the results requested from the responding application(s). Upon receiving the results, the PCP confirms that the patient’s glucose levels have been progressively increasing based on available results for each visit since 7/5/2012. The PCP then schedules a set of diagnostic tests to aid her in developing an effective rehabilitation plan to proactively manage her patient’s health condition. <span style="font-family: Arial,sans-serif; font-size: 13pt;">Patient Level Query
 * <span style="font-family: Arial,sans-serif; font-size: 11pt;"> PCP querying lab data results over past 12 months for a patient whose HbA1c is >7% **

<span style="font-family: Arial,sans-serif; font-size: 11pt;">A patient enters the hospital for pneumonia. During his visit, he is diagnosed with CHF. Patient instruction located in Application X queries the information from Application Y and receives patient demographics and admitting diagnosis, triggering a preliminary list of education topics for introduction to pneumonia and medications for in-hospital teaching. Application X then receives (either via query or as and alert) for the CHF diagnosis, and begins to queue topics for daily teaching on a new diagnosis, new medications and diet. Prior to discharge, Application X queries Application Y -- perhaps seeking a C-CDA in whatever state of completion it's available -- and topics for discharge instructions are triggered for compilation by providers.
 * <span style="font-family: Arial,sans-serif; font-size: 11pt;"> Two applications share data during a hospital visit to coordinate information about diagnoses, medications and treatments and queuing of appropriate patient education and instruction material. (Debbie Foss Submitted on Wednesday September 5th, 2013) **

<span style="font-family: Arial,sans-serif; font-size: 11pt;"> Population level Query <span style="font-family: Arial,sans-serif; font-size: 11pt;">A new physician starts working at a health center where many patients with Hepatitis C are treated. The physician is aware of clinical practice guideline that specifies that patients with Hepatitis C diagnosis on active treatment must have fasting glucose test performed at the beginning of treatment and at predefined intervals during the treatment. The physician wants to conduct research on the quality assessment of patients being treated. The physician sets up a query to first identify all patients with a diagnosis of Hepatitis C and currently receiving Hepatitis C treatment that have not had a fasting glucose test since beginning of the therapy. The query is sent from the local EHR system to an identified application(s) (i.e. Clinical Data Repository) to retrieve a list of patient names fitting these criteria. Upon receiving this information back in his EHR system the physician learns that 3% of his Hepatitis C patients currently under treatment have not had their fasting glucose test. The physician then retrieves the list of individual patients who have consented to share their information for purposes of research.
 * <span style="font-family: Arial,sans-serif; font-size: 11pt;">Physician conducts ad hoc query to determine percent of Hepatitis C patients for research at an organization under treatment with no fasting glucose lab tests (EHR to CDR) **

<span style="font-family: Arial,sans-serif; font-size: 11pt;">Patient Level Query <span style="font-family: Arial,sans-serif; font-size: 11pt;">Dr. Jones admits patient J to the hospital for pneumonia. During patient J’s visit, he is diagnosed with angina. While in the hospital, he is scheduled for angiogram. During preop, the cardiology nurse begins the data entry process into the cardiology system for the patient (e.g., completes assessment form.) The nurse selects the patients name and the cardiology system initiates a query to the EHR for demographic and patient profile data (e.g., problems, meds and allergies.) The EHR returns the information, the cardiology system uses this information to populate the assessment form, and the nurse completes any missing information through a patient interview. (During the assessment process the same information returned is used for decision support and reminders.) During the angiogram, patient J requires angioplasty. Medications are administered during the procedure and new ongoing orders are created. After the procedure is closed, the Cardiology system pushes the administered medications and ongoing medications to the EHR.
 * <span style="font-family: Arial,sans-serif; font-size: 11pt;"> User Story Revised and Submitted by Nicole Antonson September 12th, 2013 **
 * <span style="font-family: Arial,sans-serif; font-size: 11pt;">Ancillary to EHR Query and Update (Pull and push) **

<span style="font-family: Arial,sans-serif; font-size: 14pt;">**Data Access using Quality Measures** <span style="color: #ff0000; font-family: Arial,sans-serif; font-size: 14pt;">(Suggested User Story for Targeted DAF Work stream) <span style="font-family: Arial,sans-serif; font-size: 13pt;">Population level query #2 <span style="font-family: Arial,sans-serif; font-size: 11pt;">A pediatrician runs a small practice in a small town and uses a vendor’s EHR product to maintain general health information about his patient population and another 3rd party’s application to document immunization activities for his patients. He wants to ensure that he follows vaccination schedules as recommended by the Advisory Committee for Immunization Practices (ACIP) and maintain documentation on reasons why vaccines were not administered. His 3rd vendor’s application is certified for 2014 Edition Certification requirement: "§170.314(c) Clinical Quality Measures" and can therefore capture, calculate and electronically transmit aggregated and patient-specific vaccination reports for his patient population based on Quality Measure CMS_117v2 (NQF 0038). He wants to obtain a list of patient names from his patient population with incomplete vaccination schedules so that his nurse can contact these patients and schedule a visit to receive missed vaccines. **He creates a query request using his EHR system that queries 3rd party application and retrieves an aggregated report for all patients aged 2 years or younger that do not meet the NQF quality measures outlined above.** The report shows that 15% of his patients have not been vaccinated according to the ACIP guidelines. **The physician then creates a second query to obtain the list of patients missing vaccinations and associated individual vaccination records for those patients.** The physician and his nurse then contact these patients and schedule a visit for them to receive missed vaccines.
 * <span style="font-family: Arial,sans-serif; font-size: 11pt;">A pediatrician queries for immunization reports within his local EHR system to identify patients with missing vaccinations based on Quality Measures. **