Building open source safety-critical medical device platforms and Meaningful Use EHR gateways

Transcription

1 Building open source safety-critical medical device platforms and Meaningful Use EHR gateways Inherent connectivity creates significant opportunities in medical science

2 Who is Shahid? 20+ years of software engineering and multi-site healthcare system deployment experience 12+ years of healthcare IT and medical devices experience (blog at years of technology management experience (government, non-profit, commercial) 10+ years as architect, engineer, and implementation manager on various EMR and EHR initiatives (commercial and nonprofit) Author of Chapter 13, You re the CIO of your Own Office 2

3 What s this talk about? Health IT / MedTech Landscape Data has potential to solve some hard healthcare problems and change how medical science is done. The government is paying for the collection of clinical data (Meaningful Use or MU ). All the existing MU incentives promote the wrong kinds of data collection: unreliable, slow, and error prone. Key Takeaways Medical devices are the best sources of quantifiable, analyzable, and reportable clinical data. New devices must be designed and deployed to support inherent connectivity. OSS is ideal for next generation and innovative medical devices and gateways 3

4 What if we had access to all this data? Cardiac output monitors Defibrillators Fetal monitors Electrocardiographs Infant incubators Infusion pumps Intelligent medical device hubs Interactive infusion pumps MRI machines X-Ray machines Physiologic monitors Ventilators Vital signs monitors Source: Jan Whittenber, Philips Medical Systems 4

5 What problems can data help solve? Cost per patient per procedure / treatment going up but without ability to explain why Cost for same procedure / treatment plan highly variable across localities Unable to compare drug efficacy across patient populations Unable to compare health treatment effectiveness across patients Variability in fees and treatments promotes fraud Lack of visibility of entire patient record causes medical errors 5

6 Data changes the questions we ask Simple visual facts Complex visual facts Complex computable facts 6

7 Data can change medical science The old way Identify problem The new way Identify data Ask questions Generate questions Collect data Mine data Answer questions Answer questions 7

8 Evidence-based medicine is our goal Eminence Trust me Evidence Prove it 8

9 Types of medical data we care about Phenome Phenotype is a composite of our observable characteristics or traits This is what we ve been studying for centuries Genome Genotype is the entirety of our hereditary information (DNA, RNA, etc.) Genetics is the study of the genome Proteome Proteome is our set of proteins expressed by our genome and changes regularly over time Proteomics is the study of the proteome 9

10 Unstructured patient data sources Patient Health Professional Labs & Diagnostics Medical Devices Biomarkers / Genetics Source Self reported by patient Observations by HCP Computed from specimens Computed realtime from patient Computed from specimens Errors High Medium Low Time Slow Slow Medium Reliability Low Medium High Data size Megabytes Megabytes Megabytes Data type PDFs, images PDFs, images PDFs, images Availability Common Common Common Uncommon Uncommon 10

11 Structured patient data sources Source Patient Self reported by patient Health Professional Observations by HCP Labs & Diagnostics Specimens Medical Devices Biomarkers / Genetics Real-time from patient Specimens Errors High Medium Low Low Low Time Slow Slow Medium Fast Slow Reliability Low Medium High High High Discrete size Kilobytes Kilobytes Kilobytes Megabytes Gigabytes Streaming size Gigabytes Gigabytes Availability Uncommon Common Somewhat Common Uncommon Uncommon 11

12 Predictions for Device Hardware Thick Devices Thin Devices Consumerization of Devices Virtual Devices Sensors Only with Built-in Wireless Sensors on mobile phones, platforms 12

13 Predictions for Device Software Consumerization of Apps Software for algorithms Software for functionality Software for connectivity Software only 13

14 Predictions for Device Connectivity Stand-alone and monolithic Connectivity within own organization Consumerization of IT Multi-vendor connectivity System of Systems (SoS) 14

15 Predictions for Gateways Changes in Practice Models Single-purpose devices standalone Multi-purpose standalone Multi-purpose with documentation connectivity Multi-purpose with analytical connectivity Multi-purpose with cooperating connectivity 15

16 Predictions for Self-Management The Patient is in charge Physicians manage paper charts independently Physicians and Hospitals manage paper charts together Electronic Health Records (EHRs) manage data in systems Patients manage their own data Health Information Exchange allow coordination 16

17 Implications Make sure the patient is in the middle Move from hardware to software focus Move to algorithms and analytics Understand system of systems (SoS) Plan for integration and coordination Start building simulators 17

18 OSS revolution in device design Device Components 5 Web Server, IM Client 3 rd Party Plugins Presence Messaging Registration JDBC, Query 6 App #1 App #2 Sensors Storage Display Plugins Event Architecture 4 Connectivity Layer (DDS, HTTP, XMPP) 7 Location Aware 3 Plugin Container Device OS (QNX, Linux, Windows) 1 2 Security and Management Layer 18

19 OSS revolution in device capabilities Most obvious benefit Least attention Most promising capability 19

20 OSS revolution in Gateways Corporate Cloud (Data Center) Apps Services Development Remote Facilities VPN Customers & Partners Apps MQs Services HTTPS, SOAP, REST, HTTP SFTP, SCP, HL7, X.12 SMTP, XMPP, DDS Registry HTTPS, REST, SOAP SFTP, SCP, HL7, X.12 SMTP, XMPP, DDS Corporate Gateway (ESB) App DB Service DB Central DB Firewall Security Management Services NOTE: Initial design is for a non-federated backbone. If performance or security demands require it, a federated solution will be deployed. 20

21 OSS revolution in integration Device Teaming Cloud Services SSL VPN Patient Self-Management Platforms Patient Context Monitoring Device Gateway (DDS, XMPP, ESB) Device Data Data Transformation (ESB, HL7) Cross Device App Workflows Remote Surveillance Management Dashboards Enterprise Data Alarm Notifications HIT Integration Report Generation Device Management Inventory 21

22 OSS revolution in manageability Security Is the device authorized? Teaming Device grouping Inventory Where is the device? Presence Is a device connected? 22

23 Key OSS questions Will the FDA accept open source in safety-critical systems? Is open source safe enough for medical devices? 23

24 Simple answers Will the FDA accept OSS in safetycritical systems? Is OSS safe enough for medical devices? Yes Yes but you must prove it 24

25 It s not as hard as we think Modern real-time operating systems (open source and commercial) are reliable for safetycritical medical-grade requirements. Open standards such as TCP/IP, DDS, HTTP, and XMPP can pull vendors out of the 1980 s and into the 1990 s. Open source and open standards that promote enterprise IT connectivity can pull vendors into the 2010 s and beyond. 25

26 But it s not easy either we need Risk Assessments Hazard Analysis Design for Testability Design for Simulations Documentation Traceability Mathematical Proofs Determinism Instrumentation Theoretical foundations 26

27 OSS / open standards applicability Project / Standard Subject area D G Comments Linux or Android Operating system Various distributions OMG DDS (data distribution service) Publish and subscribe messaging AppWeb, Apache Web/app server Open standard with open source implementations OpenTSDB Time series database Open source project Mirth HL7 messaging engine Built on Mule ESB Alembic Aurion HIE, message exchange Successor to CONNECT HTML5, XMPP, JSON Various areas Don t reinvent the wheel SAML, XACML Security and privacy DynObj, OSGi, JPF Plugin frameworks Build for extensibility 27

28 @ShahidNShah Thank you Conclusion and Questions