Penn Medicine Academic Computing Services
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IS Leadership Insights from Brian Wells

Leadership Insights

I recently had the pleasure of “predicting the future” (well at least the very near future) for Healthcare IT News (http://www.healthcareitnews.com/blog/6-predictions-2016). Much of what I discussed are active projects at Penn Medicine today. In 2016, we will see a record amount of software and technology implementation within the School of Medicine. Our collaborative IT governance approach, shared belief in the value of information technology, financial commitment and engaged faculty and staff have created an energy level that drives momentum, is contagious and ensures success. We will complete these initiatives in 2016:

  • Develop a self-service, intuitive set of consolidated business analytics dashboards to enable PSOM administrators to optimize PSOM performance
  • Expand the use of Velos clinical trial management system (CTMS) beyond the Cancer Center into several departments that generate large volumes of clinical trials
  • Integrate the Velos CTMS with Epic such that patient demographic data, study data and subject enrollment status is automatically passed between the two systems thereby eliminating double data entry and ensuring accurate tracking of clinical research subjects
  • Implement a document management system (DMS) for the transparent creation, tracking and storage of critical clinical trial documentation (contracts, protocols, consents, etc.)
  • Replace the legacy PSOM account management systems with the new Centralized Account Management System (CAMS) to create a single, consistent, easy to use tool for business administrators to authorize spending against grant and other funds
  • Implement a natural language processing (NLP) solution that will enable us, in targeted use cases, to derive clinical facts from unstructured textual notes and reports for use in retrospective analysis, clinical trial recruiting or improved abstraction into registries
  • Develop an interactive molecular decision support application that can be used by oncologists (and eventually other specialties) to understand and apply genetic results to the precision care of Penn Medicine patients and research subjects
  • Continue to convert legacy bio-bank inventories into the LabVantage laboratory information management system (LIMS)
  • Creation of a new tool to track the request to recruit (RTR) processes and documents for Faculty Affairs
  • Replacement of the existing event calendar system with a new vendor supplied solution.

This impressive collection of initiatives underway simultaneously and on top of the continued care and feeding of existing solutions like Penn Data Store, PennOmics, PennSeek, Faculty Analytics, Expertise@Penn is a testament to our investment in the consolidation of the information technology teams across Penn Medicine. The implementation of any new solution requires equal focus on people, process and technology. It is the deep and broad collaboration with our staff and faculty partners that is so critical to our joint success.

Metrics

HPC Statistics (December 2015)
  • CPU Hours — 1,227,097
  • Disk (TB) — 1468
  • Archive(TB) — 300
  • Total Number of Users — 170
LIMS Statistics
  • Total Number of Users — 43
  • Total Samples — 343,503
ACC Velos Statistics
  • Total Studies — 2,780
  • Total Subject ~ 75,000
  • Total Active Accounts — 300
Customer Service Group (CSG) Tickets
  • Total Tickets — 10,859, FY 2016 Year to Date

Technology Initiatives

Administration

New PSOM event calendar

Last fall, a team assembled (from Dr. Jon Epstein's office, Space Planning, and PMACS) to select a new events calendar. LiveWhale Calendar (http://www.livewhale.com/calendar/) was selected to address the needs of the school. The product features a much improved user interface, better options for integrating events calendars into web sites, and easier integration with other applications. We join Penn Law and the Nursing school in adopting this calendaring product.

Currently, LiveWhale Calendar is being customized for the Perelman School. A pilot group will also test the product and help identify documentation and customization opportunities. The implementation will include pulling some events information from the new room reservation system (EMS).

The tentative implementation timeline for the school begins March 2016.

Learning Health System Award

Penn Medicine won the “Learning Health System (LHS) Pioneer Award” from the American Association of Medical Colleges (AAMC) Research on Care Community (ROCC). The Research on Care Community offers the Learning Health System Pioneer Award to AAMC member institutions that identify an interest in developing innovative, system-wide processes that improve the opportunity for research and enhance health system and research collaboration but have not yet fully implemented or significantly scaled up improvements.

Applicants submit from institutions that have critical infrastructure and are developing processes that enhance research and health system collaboration. Applications focused on system-wide processes that improve the opportunity for research in the following areas, such as:

  • Care models which incorporate real time data exchange between patients and care teams (e.g., iPhone/android apps, tele-monitoring, asthma, diabetes, or other condition tracking);
  • Mobile device apps with health data monitoring and data sharing capabilities; or
  • Electronic Health Record data systems which:
    • Allow for visualization and analysis of EHR data;
    • Facilitate research data capture in clinical workflow;
    • Collect patient reported measures for treatment planning, population management, and research; or
    • Link EHR data with genomic data for research.
    • (AAMC Website, 2016)

    Penn Medicine's 2015/2016 submission for the Pioneer Award consisted of a summary of a number of initiatives that are planned or are underway to align the clinical and research missions of the organization. These projects serve to aggregate, normalize, share, and analyze clinical and research data that were previously dispersed and dissimilar. Initiatives include both centralized clinical and research data warehouses, a natural language processing system, a centralized Lab Information Management System, a centralized Clinical Trials Management System, a centralized document management system primarily serving clinical trial needs, a central bio-bank, and a center for personalized diagnostics.

    The ultimate goals of these initiatives are to enhance research and clinical outcomes using improved processes and systems, and the transformation of big data into enhanced value. Some of the steps necessary to realize the full value of the program are complete, while others are in progress/onboarding. Key aspects of the program are:

    1. Integration of clinical and academic/research IS departments (completed). IS departments that were previously separate are now unified under one leadership model and one CIO. This allows a more streamlined process of data sharing, security oversight, as well as integration of projects and initiatives between the two departments. The centralized leadership model ensures the work both departments are pursuing aligns with the overall strategic goals of both research and clinical domains. The resulting department is 580+ individuals covering the full spectrum of IT services including clinical information systems, research information systems, security, device and infrastructure support, project management, and development.
    2. Aggregation of clinical data into data warehouse (completed). Known as the Penn Data Store, this data warehouse contains over 4 billion rows of data aggregated from 12 different clinical systems. Initially leveraged for purely clinical use, researchers and clinicians now access and analyze these data for both research and clinical purposes. Broadening the accessibility and use of these data greatly increases the value of these data.
    3. Implementation of enterprise research Lab Information Management System (onboarding). An enterprise lab information management system provides a structured, secure, managed, and centralized service by which researchers can manage bio-sample inventory information, bio-sample metadata, and assay results. Aggregating data, while maintaining a hybrid centralized/decentralized physical storage environment enables more effective use of bio-sample data, while not requiring the physical aggregation or centralization of samples. The system continues to accept new labs, and currently tracks over 350,000 samples.
    4. Implementation of Research Data Warehouse (onboarding). Known as PennOmics, the research data warehouse focuses on research data/analysis. This is a self-service system, and contains over 3 million de-identified patients, 6,000 genetic sequences, and nearly 1.7 trillion variants. Other clinical and research systems feed PennOmics, and as more data is aggregated in new and existing enterprise systems, the amount of data and the value derived from these data will continue to increase.
    5. Common Ambulatory and Inpatient EMR (2017). Currently, the ambulatory EMR and inpatient EMR are disparate systems. This project, which is underway, will centralize EMR functions under one system and one vendor. This data centralization will enable process improvements and streamlining with respect to system use, as well as harmonize inpatient and ambulatory data within one system and one database.
    6. Enterprise Clinical Trials Management System (onboarding & expanding). In use for nearly seven years in the Abramson Cancer Center, the benefits of a centralized Clinical Trials Management System lead to improved regulatory compliance, study data aggregation, visibility into study status and study progress, and financial oversight. Expansion to encompass the entire research enterprise is underway, resulting in a planned active study count of 2,500 over the next 18-24 months.
    7. Document Management System/eTMF management (2016). Current document management in the clinical trials research space is manual, and relies on both electronic and paper documentation. To improve compliance, accuracy, and provide economies of scale, the organization has selected a document management system to serve as the process and document aggregation point for clinical research studies. The system will manage the electronic trial master file (eTMF) in total.
    8. Natural Language Search System (onboarding). Known as PennSeek, this natural language search system stores 98 million unstructured notes, scripts, and documents available for searching and analysis. The system aggregates data from EMR encounters, radiology, pathology and cardiology reports, and prescriptions. Currently 29 IRB approved studies use the system in their study design.

    As these initiatives proceed, Penn Medicine faculty and staff will have greater access to valuable data in a more easily consumable format that was previously available. The monetary prize associated with the award is planned to partially fund a junior faculty member focusing on bioinformatics research.

    How the “agile” methodology supports the governance process

    Discussions of the value of Agile project management techniques often focus on how team performance and project management will be improved. Though our experience bears this out, the PMACS Web Development and Design team has also discovered that the techniques and data collected are extremely valuable in providing 1) decision-making tools for governance 2) capacity planning at senior levels.

    Using Agile/Scrum tools, we have been able to provide better assessments of new projects. This includes describing scope, requesting additional resources when necessary, and developing preliminary schedules. The Perelman School's computing advisory boards are then able to use this information in assessing projects they wish to advance to governance committees.

    Once a project is approved, we use Agile/Scrum information to report on progress to the project owners and governing groups. These include progress reports, changes in scope, and how those changes may effect the delivery timeline so that informed decisions can be made about how to proceed. From the backlogs, we are able to compile recent accomplishments, active work, upcoming activities and display the relevant data using techniques like 'burndown' charts.

    Research

    Announcing winners of the 2015 PennOmics Data Wizard Contest

    Daniel Kiss et al received the grand prize for the genetic category with their submission “The Role of Apolipoprotein L1 in Atherosclerotic Cardiovascular Disease” which took advantage of the combined clinical and genomic data within PennOmics to evaluate the connection between polymorphisms in ApoL1 in African Americans and their associated cardiac events.

    Sony Tuteja and Sean Hennessy received the grand prize for the clinical category for their submission “Identification of patients that may benefit from Pharmacogenetic testing to prevent adverse drug reactions” This preliminary study found the patients who received medications known to induce drug-induced hypersensitivity reactions with corresponding diagnosis codes that indicated an adverse reaction.

    Two honorable mentions were given for “Red cell distribution width (RDW) and the development of systemic inflammatory response syndrome (SIRS)” by Eric VonHerbulis et al and “Using PennOmics to Assess Long Term Cardiovascular Disease Following a Pregnancy Complicated by Hypertension” by Chileshe Nkonde-Price and Zeeshan Khan.

    We would like to thank all contestants for their participation and hope they will submit again. Both grand prize winners will receive $10,000 for their submissions and the honorable mentions will receive $5,000.

    PennOmics is a translational research data warehouse that integrates research and clinical data from many sources across Penn Medicine. It includes rich, de-identified clinical data on over 3.1 million patients including somatic tumor sequence data from the Center for Personalized Diagnostics and genomic data from contributing labs. Users may search and export a de-identified set of this data using the Cohort Explorer user interface without IRB approval. To gain access, please contact Renae Judy at renae.judy@uphs.upenn.edu.

    The PennOmics Data Wizard competition will be held again in 2016. Look for an announcement in the coming months.

    User Tip

    The Health Insurance Portability and Accountability Act, (HIPAA), Privacy Rule protects all "individually identifiable health information" held or transmitted by a covered entity or its business associate, in any form or media, whether electronic, paper, or oral. The Privacy Rule calls this information "protected health information (PHI)."

    The HIPAA Security Rule refers specifically to electronic protected health information, (ePHI).

    “Individually identifiable health information” is information, including demographic data, that relates to:

    • The individual's past, present or future physical or mental health or condition,
    • The provision of health care to the individual, or
    • The past, present, or future payment for the provision of health care to the individual, and that identifies the individual, or for which there is a reasonable basis to believe can be used to identify the individual.

    Basically, PHI/individually identifiable health information becomes such when the above information is associated with any of the HIPAA common identifiers, listed below. In essence, an individual's medical history or medical payment history along with any of the common identifiers is considered to be PHI since it could potentially be used to identify the individual and associate him/her with the health care related information.
    Note, health care information without identifiers is not PHI.

    PHI = (Physical or mental health or condition information, or Provision of health care information, or Provision of health care payment information) + (Identifier)

    List of 18 Identifiers
    1. Names.
    2. All geographical subdivisions smaller than a State, including street address, city, county, precinct, zip code, and their equivalent geocodes, except for the initial three digits of a zip code, if according to the current publicly available data from the Bureau of the Census: (1) The geographic unit formed by combining all zip codes with the same three initial digits contains more than 20,000 people; and (2) The initial three digits of a zip code for all such geographic units containing 20,000 or fewer people is changed to 000.
    3. All elements of dates (except year) for dates directly related to an individual, including birth date, admission date, discharge date, date of death; and all ages over 89 and all elements of dates (including year) indicative of such age, except that such ages and elements may be aggregated into a single category of age 90 or older.
    4. Phone numbers.
    5. Fax numbers.
    6. Electronic mail addresses.
    7. Social Security numbers.
    8. Medical record numbers.
    9. Health plan beneficiary numbers.
    10. Account numbers.
    11. Certificate/license numbers.
    12. Vehicle identifiers and serial numbers, including license plate numbers.
    13. Device identifiers and serial numbers.
    14. Web Universal Resource Locators (URLs).
    15. Internet Protocol (IP) address numbers.
    16. Biometric identifiers, including finger and voice prints.
    17. Full face photographic images and any comparable images; and
    18. Any other unique identifying number, characteristic, or code (note this does not mean the unique code assigned by the investigator to code the data).

     

    Remember, security won't work without you. YOU are the key to security at PSOM.

    Please refer all information security comments or concerns to David Wargo: David.Wargo@uphs.upenn.edu
    For more security related information, visit the PMACS Information Security web page: http://www.med.upenn.edu/pmacs/

    Laptop Security

    Whenever a data breach occurs, more often than not, a laptop or other portable device was involved. Criminals continue to take advantage of our mistakes, which include storing sensitive information on devices, leaving portable devices unattended in public areas, and not using whole disk encryption.

    Did you know that?

    • A laptop has about a 1 in 10 chance of being stolen.
    • About half of all laptop thefts occur in offices or classrooms.
    • A high percentage of stolen laptops are never recovered.

     

    How can you protect your portable devices and the information residing on them?

    1. Don't store sensitive information on a portable device.
      1. If sensitive information, e.g. ePHI, must temporarily be stored on a PMACS managed device, the device must be encrypted using whole disk encryption, e.g. BitLocker, on Windows devices, or FileVault, on Mac devices. It is also recommended that this security control be implemented on personally owned devices, as needed. In general, whole disk encryption is a very good idea since you will most likely, at least occasionally, have some type of sensitive information on your device.
    2. Ensure that Absolute Data & Device Security, (formerly Computrace), is installed on the device. This product can be used to trace the device if it is lost or stolen, and wipe the device if necessary.
      1. In the event of a device theft or loss, immediately report the incident to the local or University police, after which you should contact PMACS about the incident.
    3. Ensure that virus protection is on the device and being updated in a timely manner.
    4. Ensure that security updates are installed in a timely manner.
    5. Ensure that the software firewall is enabled.
    6. When unattended, secure portable devices in a locked office or cabinet.
    7. Traveling?
      1. When flying consider a portable device to be carry-on luggage, and don't leave it alone even for a few seconds.
      2. Choose an inconspicuous carrying case.
      3. Don't put the device in the trunk of a cab.
      4. Don't leave the device in plain view when using a personal or rental vehicle, or when left in your hotel room.
    8. If you must have a list of passwords and PINS keep the list separate from the device.

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