As part of the mini-symposium entitled “Research on Digital Health for Designing Scalable Pervasive Healthcare Monitoring, Rehabilitation, and Home-based Healthcare Systems,” Dr. Mathew discusses the research and clinical data ecosystem at UMass Medical School and the D3Health system integrating biomedical big data, analytics, and decision support

Mathew, Jomol

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University of Massachusetts Medical School eScholarship@UMMS UMass Center for Clinical and Translational Science Research Retreat 2017 UMass Center for Clinical and Translational Science Research Retreat May 16th, 10:15 AM UMMS Biomedical Data Assets & D3Health Jomol Mathew University of Massachusetts Medical School Let us know how access to this document benefits you. Follow this and additional works at: https://escholarship.umassmed.edu/cts_retreat  Part of the Health Information Technology Commons, and the Translational Medical Research Commons Mathew J. (2017). UMMS Biomedical Data Assets & D3Health. UMass Center for Clinical and Translational Science Research Retreat. Retrieved from https://escholarship.umassmed.edu/cts_retreat/2017/program/6 Creative Commons License This work is licensed under a Creative Commons Attribution-Noncommercial-Share Alike 3.0 License. This material is brought to you by eScholarship@UMMS. It has been accepted for inclusion in UMass Center for Clinical and Translational Science Research Retreat by an authorized administrator of eScholarship@UMMS. For more information, please contact Lisa.Palmer@umassmed.edu. UMMS	Biomedical	Data	Assets	&		D3Health	Jomol	Mathew,	Ph.D.	May	15,	2017	Disclosures:I	have	no	actual	or	potenGal	conﬂict	of	interest	in	relaGon	to	this	program/presentaGon.	University of Massachuse6s is Uniquely Posi9oned to be a Game Changer in HealthcarePathways	to	Clinical	IntegraGon	World-class	Biomedical	Research	World-class	Health	InformaGcs	ExperGse	in		Digital	Health	&	Sensors	Ethnically	Diverse		PopulaGon	In	Central	Mass	Pa9ents generate useful data that is not available  to health care providersA	B	C	M	N	We will break down barriers to integrate data & make it available for clinical decision makingPatients Patient self Reported/Device & Clinical Data Research Data Biospecimen Banking Clinical Decision Support Informed Clinical Trials Visualization & Analytics   Our Clinical and Transla9onal Research Data Ecosystem Vision: Build an Integrative Clinical & Biospecimen Data Ecosystem to: •  Enable data driven research •  Enable translation of research findings to clinical care •  Make a difference in community and global health Data Lake PATIENT CARE  PROVIDER RESEARCHER Health System as a Learning Lab Data	Lake	holds	Clinical	data	from	>2.5	Million	paGents	representaGve	of	naGonal	diversity	•  A	rich	source	of	data	for	studying	chronic	diseases	(e.g.	cardiovascular,	diabetes)	•  A	useful	tool	to	study	geneGc	diseases	(e.g.	CysGc	Fibrosis,	Parkinson’s)	•  A	mechanism	to	link	biosamples,	molecular	data	&	digital	health	data	to	clinical	data	UMMS Data Lake				Smart/Sensor	Data	Home		monitoring	of	paGent	wellness	and	disease	data;	environmental	data	Hospital	EHR	Data	D3Health:	IntegraGng	Biomedical	Big	Data,	AnalyGcs,	&	Decision	Support	PATIENT	FEEDBACK	PROVIDER	FEEDBACK	1More	EﬀecGve	Precision	Therapy	IntervenGon	Alerts		(e.g.	Alter	MedicaGon)	Targeted	Clinical	Trials	Community	Alerts	(e.g.	InfecGon	outbreaks)	PaGent	Alerts	(e.g.	changes	in	Transthoracic	Bioimpedance)	4Data	Collec.on			Data	IntegraGon	AggregaGon	Data	Lake	Algorithms	ArGﬁcial	Intelligence	Pacern	RecogniGon	Predic.ve	Analy.cs	Knowledgebase	with	AcGonable	IntervenGon	Points	&	GeneraGon	of	Feedback	Loops	Decision	Support	Data	from	Remote/Sensors	is	integrated	with	clinical	data	Knowledge	is	generated	and	acGonable	intervenGon	points	are	idenGﬁed	2 3a.enteedback4rovider	eedback4CLINICAL CARE EDUCATION RESEARCH D3Health What Treatment is Be6er for Me/My Pa9ent?Why	do	some	paGents	respond	well/poor	to	speciﬁc	drugs?	What	are	the	PotenGal	Adverse	Events	for	the	new	study?	Why	does	the	disease	progress	faster	for	some	paGents?	Am	I	Eligible	for	a	new	study?	How	do	I	compare	to	other	paGents?	All	there	clinical	trials	targeGng	the	speciﬁc	geneGc	alteraGons	found	in	this	paGent?	What	are	the	treatment	opGons?	Why	do	some	paGents	develop	resistance	to	drugs?	What	is	involved	in	the	study?	Data Lake & D3Health will helps us studyDiseases….. Con9nuum of care…..Con9nuous Learning Cycles…	Is	this	condiGon	geneGcally	transferable?	What	causes	this	disease?	Are	there	sub-classes	within	this	disease?	How	does	this	PaGent		compare	to	other	paGents?	What	is	the	best	course	of	treatment?	What	is	the	biological	underpinning	of	this	disease?	•  Enables	Researchers	to	get	a	list	of	paGents	who	have	consented	to	be	contacted	about	upcoming	studies		Expanding	via	•  Social	media		•  Special	populaGon	resource	center	•  Direct	to	paGent	tools	•  RecruiGng	via	EHR	once	EPIC	in	place	Data Ecosystem Components: Volunteer Registry & e-Consen9ng SystemData Ecosystem Components: OpenSpecimen for BioBankingSingle	Shop	for	Biospecimens	•  Consent,	collect	&	barcode	•  Create	derivaGves	&	keep	lineage	•  Search	&	ﬁnd	•  Scan	&	distribute	•  Link	to	clinical	data	in	Data	Lake	&	facilitate	query	and	request	of	biospecimens	from	central	biobanks	(blood,	tumor,	microbiome)	Data Ecosystem Components: LabArchives for Collec9on & Management of Research Data•  Electronic	Lab	Notebook	•  Enables	easy	access	to	data	between	lab	members	and	collaborators	•  Supports	secure	data	trail	(necessary	for	commercializaGon)	•  ~	300	Users	are	using	LabArchives	Data Ecosystem Components: Synergist for Searching & Sharing Research Data“Amazon”	of	Research	data	•  Catalog	&	Share	Experimental	Metadata	•  Search	and	Discover	Data	&	Collaborate	•  Connect	&	Gain	Insights	•  Publish	&	Submit	Data	to	External	Data	Banks	Data Ecosystem Components: OnCore Clinical Trials Management System•  A	leading	CTMS	plaiorm	at	Academic	Medical	Centers	•  Integrates	well	with	EPIC	to		•  improve	paGent	safety	•  Improve	protocol	compliance						• 	Went	live	on	10/21/16	• 	Onboarding	complete	by	07/01/2017	• 	EPIC	IntegraGon	&	go-live	by		10/01/2017			EPIC	PaGent-Centric	ONCORE	Study-Centric	EHR	Primary	Concerns:		Comprehensive	management	of	a	pa.ent	over	.me			Provide	high-quality		paGent	care		PaGent	safety			Clinical	user	eﬃciency		and	producGvity		Research	billing	compliance	(ability	to	separate	clinical	and	research	charges)				CTMS	Primary	Concerns:		Comprehensive		management	of	a	study		Catalogs	and	tracks	all	clinical	trial	processes		AdministraGve	acGviGes	e.g.,	budgeGng,	approval	tracking,	study	design,	randomizaGon		InvesGgator	compliance	to	administraGve	requirements		Subject	protocol	compliance			Direct	reporGng	to	sponsors		Overlapping	Needs:		- 	Basic	study	informa.on	- 	Pa.ents	associated	with	studies	- 	Research	billing	deﬁni.on	for	study		Data Ecosystem Components: ONCORE Clinical Trials Management System & EPIC Integra9onONCORE & EPIC Integra9on: Empowers Research & Care•  Benefits for Researchers •  Achieve better recruitment for trials •  Study Teams can track the subjects  •  Streamline collection of biospecimens •  Benefits for Care Providers •  See that a patient is on a clinical trial in the banner •  Get detailed information on the protocol •  Identify clinical trials for patients •  Benefits for Patients  •  Get access to latest care especially when options are limited PATIENT CARE  PROVIDER RESEARCHER Health System as a Learning Lab Quan9fying Pa9ent Experiences: Pa9ent Reported Outcomes•  PaGent	Reported	Measures	•  Compendium	describing	PRM	use	across	UMass	community	to	facilitate	prioriGzaGon	of	Epic	build	•  Domain	Examples:	QoL,	mental	health,	physical	funcGoning,	pain,	PTSD,	Tobacco/Alcohol/Drug	use,	etc.		•  Sepngs:	InpaGent,	OutpaGent,	ED	•  PopulaGons:	Adults,	Pediatrics,	Psych	•  PRM	administraGon	•  Currently	Collected	using:	REDCap	•  Working	with	Epic	Team	to	prioriGze	and	build	Honest	Broker:	Connect	Research	&	Clinical	Data	Clinical Data Repository User queries for clinical information Aggregate/  De-identified Data Corresponding Patient Set generated De-identified Patient Set OpenSpecimen	LabArchives	Synergist	Identified Patient Set Honest Broker System Identified Data -  Enables	integraGve	queries	&	extracGon	-  Avoid	duplicaGon	of	data	-  Full	Traceability	&	Accountability		-  Compliance	with	HIPAA	&	regulaGons	Rules for Diving into the Data Lake: Data Access Policies and Processes @ UMMSKey	Policies:	•  Aggregate	or	De-iden.ﬁed	data:	No	IRB	approval	required	•  Protected	Health	informa.on	(PHI):	IRB	approval	required	•  BAA	&	Security	clearance	required	while	engaging	third-party	vendors	Who	Can	Access	Data:	•  Research	•  Faculty:	Instructor	or	above	•  Any	member	of	a	research	team	•  Quality	&	OperaGons	•  Administrators	and	staﬀ	at	UMMHC	or	UMMS		www.umassmed.edu/IT/CDP	How:	EHR Data for Clinical Trial Feasibility & Iden9fy Cohorts  37	structured	interviewees	in	Germany,	UK,	Switzerland,	and	France	indicated	strong	support	for	the	proposed	Electronic	Health	Records	for	Clinical	Research	(EHR4CR).	All	interviewees	reported	that	using	the	plaRorm	for	assessing	feasibility	would	enhance	the	conduct	of	clinical	trials	and	the	majority	also	felt	it	would	reduce	workloads.	UMMS: DLR Can Help Researchers in Be6er design of Trials & Iden9fy Cohorts e.g.	Botulinum	Toxin	for	Pelvic	Pain	in	Women	With	Endometriosis	(NCT01553201)		INCLUSION	CRITERIA:	• 	Female	gender	• 	Age	between	18	and	50	• 	History	of	endometriosis		• 	Persistent	pelvic	pain	for	at	least	3	months	• 	Pelvic	ﬂoor	spasm	• 	NegaGve	pregnancy	test	• 	Willing	to	use	reliable	method	of	contracepGon	for	the	month	arer	botulinum	toxin	injecGon	• 	Willing	and	able	to	give	informed	consent	• 	Willing	and	able	to	comply	with	study	requirements	EXCLUSION	CRITERIA:	• 	Women	with	other	causes	of	chronic	pelvic	pain	including	infecGous,	gastrointesGnal,	psychological	disorders,	ﬁbromyalgia	and	chronic	faGgue	syndrome	based	on	review	of	medical	history	within	1	year	of	ﬁrst	study	visit*.	• 	Untreated	severe	cervical	dysplasia	or	other	gynecologic	condiGon	within	the	past	year	based	on	medical	record	review*.	• 	Signiﬁcant	abnormaliGes	in	the	physical	or	laboratory	examinaGon	including	renal	and	liver	funcGon	more	than	twice	the	normal	range	• 	Hysterectomy	and	bilateral	salpingo-oophorectomy	• 	Pregnancy	• 	LactaGon	• 	Allergy	to	albumen	or	botulinum	toxin	• 	Presence	of	anGbodies	to	botulinum	toxin	or	loss	of	response	to	previous	injecGons	for	any	indicaGon	• 	A	known	neuromuscular	juncGon	disorder	such	as	myasthenia	gravis	or	Eaton-Lambert	syndrome	• 	History	of	urinary	or	fecal	inconGnence	• 	Known	pelvic	prolapse	• 	How	does	protocol	design	(exclusion/inclusion)	impact	recruitment?	• 	How	can	other	sites	selected	for	a	mulGsite	study?	Coming	Soon………CTSA	Recruitment	RFP	–	Improve	Recruitment	using	Data	Lake	UMMS: How to do Feasibility/Recruitment? Use DLR & Aggregate Search Capabili9es INCLUSION CRITERIA: •  Female gender •  Age between 18 and 50 •  History of endometriosis  EXCLUSION CRITERIA: •  Fibromyalgia  •  Cervical dysplasia •  Renal and liver function Integrated Clinical-Genomic Searches e.g. Female, Asian, NSCLC with EGFR muta9ons receiving Tarceva   - currently not on other trials - consented for future contact CURRENT		Q2	2017	Q2	2017	www.umassmed.edu/IT/CDP	ComorbidiGes	Concomitant	MedicaGons	Labs	UMMS: How to do Feasibility/Recruitment? Use DLR & Explore De-iden9ﬁed DataEHR Data Can Enable Compara9ve Eﬀec9veness Studies  EHR Data Can help in Risk Predic9on StudiesEHR Data along with Biospecimens and Genomic data can aid in Pharmacogenomics studiesEHR Data Can Enable Drug Repurposing StudiesEHR	Data	Can	Enable	Quality	Improvement	Studies	UMMS: Researchers can Obtain Detailed PHI/PII Data from DLRHIPAA	Waiver/	Authoriza.on	Inves.gator	Study	Plan/DCF	Clinical	Data	Request	Form	IRB	Documents	Required	to	Obtain	PHI/PII	Data	for	Research	Purposes	Must	be	Consistent!	+	 +	www.umassmed.edu/IT/CDP	Study	DataMart	SAS,	R….	Fully	Automated	Process	By	Dec	2017		If	you	are	requesGng	idenGﬁable	data	(PHI/PII)	from	the	DLR,	you	must	do	so	under:	•  A	HIPAA	Waiver	and/or	HIPAA	AuthorizaGon	•  Other	appropriate	documentaGon			Thanks to….•  Advisory	Commi,ee	(K.	Luzuriaga,	C.	Kiefe,	S.	Corvera,	M.Koziel,	N.	Hafer,	G.	Wolf)	•  Clinical	Research	Task	Force	(P.	Muldoon	&	M.	Koziel	Chairs)	•  Clinical	Trial	Management	Steering	Group	(M.	Koziel	&	J	Mathew	Chairs)	•  EPIC	Research	Integrated	Work	Group	(T.	Houston	&	M.	Koziel	Chairs)	•  UMMHC	IT	(T.	Tarnowski	&	T.Eglin)	•  My	Data	Science	&	Technology	Team	

UMMS Biomedical Data Assets & D3Health

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UMMS Biomedical Data Assets & D3Health

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