Transparent Data Supply for Open Information Production Processes Laine, Sami Aalto University Espoo, Finland [email protected] Lee, Carol Northeastern University Boston, Massachusetts, USA [email protected] Nieminen, Marko Aalto University Espoo, Finland [email protected] i The 23rd European Conference on Information Systems (ECIS 2015) FULL-TEXT DOWNLOAD
Transparent_Data_Supply_for_Open_Information_Production_Processes
Aug 11, 2015
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Transparent Data Supply for
Open Information Production Processes
Laine, Sami
Aalto UniversityEspoo, Finland
Lee, Carol
Northeastern UniversityBoston, Massachusetts, USA
Nieminen, Marko
Aalto UniversityEspoo, Finland
The 23rd European Conference on Information Systems (ECIS 2015)
FULL-TEXT DOWNLOAD
Personal background combines technical, human and healthcare perspectives
University of Turku, Finland• Information systems• Empirical field studies
in hospital focusing on the use of IT.
Turku University Hospital, Finland• Healthcare
datawarehousing• Project management,
system and service design.
Aalto University, Finland• Usability Research• Healthcare data and
information quality
Over 10 years healthcare IS research and development
The Finnish Hospital Productivity Benchmarking has a long history – but it is not used in decision making
The benchmarking results are produced by National Institute for Health and Welfare (THL) on annual basis.
The background, implementation and future plans of the BMS have been described earlier by Linna and Häkkinen.
They noted that policymakers and managers do not regularly use efficiency analyses and the main reason appears to be concern about
data quality.
Data Results
Open Data and Open APIs have been recognized as valuable approaches for society and business.
The validity of data driven decision making can be questioned due to inaccurate data and insufficient
provenance knowledge.
Multi-disciplinary agreement that
secondary users need to know how and why data were
created
Open Data and API approaches favor a
simplistic and idealized view about
benefits resulting from access to data
Research Agenda
We wanted to explain why Open Data approaches should pay more attention to Information in Context.
Explanatory Case Study
CaseTwo Finnish hospital districts and their respective university
hospitals.
Population coverage almost 500,000 residents and over 20
surrounding municipalities.
Methods
Preliminary interviews
Focus groups
Data Analysis
Case Study Quality
Validity
Five of the six potential types of triangulation: data source, data type, methodology, theory, and analysis.
Generalizability
We believe that our current analyses about data creation
situations could be replicated with other data elements.
Research Question
”08:35”
What does this
really mean
?
How exactly is a registration timestamp value such as “08:53” created in hospital processes?
Arrival Registration Treatments Discharge Departure
How exactly is a registration timestamp value such as “08:53” created in hospital processes?
MEANING USER TASK TOOL ENVIRONMENT
“Arrival at location” Patient Self-registration Barcode card Current unit
“Available service at reception”
Secretary(current user)
Registration EPR & key press Current unit
“Midnight at previous day”
Secretary(current user)
RegistrationEPR & manual
adjustmentCurrent unit
“will leave at this time”
Secretary(at previous unit)
DischargeEPR & manual
adjustmentPrevious unit
“will be picked up at this time”
Secretary(at previous unit)
DischargeEPR & manual
adjustmentPrevious unit
“is leaving unit now”Secretary
(at previous unit)Discharge EPR & key press Previous unit
The same data value can mean completely different things, but they all look identical at data layer!
Registration at ”08:35”
“Arrival at location”
“Available service at reception”
“Midnight at previous day”
“will leave at this time”
“will be picked up at this time”
“is leaving unit now”
Even a simple data element can be complex information!
Registration timestamps that look like “availability of reception” but are actually “arrival at location”!
SUPPLY PHASE CREATE COLLECT RECORD
USER Patient EPR Secretary
TASK Self-registration Data integration Registration
TOOL Barcode card Registration Device EPR
ENVIRONMENT Current unit Current unit Current unit
Missing!Open Data
“Arrival at location”
“Available service at reception”
“Midnight at previous day”
“will leave at this time”
“will be picked up at this time”
“is leaving unit now”
Registration timestamps that look like “available service at reception” but are actually “will leave at this time”!
SOFTWARE LAYER
User Interface Application Database
USERSecretary
(at previous unit)
EPR Secretary(current)
TASK DischargeEnforce business
ruleRegistration
TOOLEPR & manual
adjustment
Software code
EPR & timestamp
ENVIRONMENTPrevious
unit Data center Current unit
Missing!Open Data
“Arrival at location”
“Available service at reception”
“Midnight at previous day”
“will leave at this time”
“will be picked up at this time”
“is leaving unit now”
Analysis
Timestamp accuracy problems
Multiple data entry techniques
Obscure data creation situations
Ambiguous and inconsistent definitions
Human errors and motives
Human behavior patterns
Requirements for Transparent Data Supply
Quality Controls
Precise Semantics
Documented Contexts
Automatic Supply
Traceable Contexts
Openness
Implications
Open Data and APIs focus mainly on Data Layer
18
USER INTERFACE
APPLICATION
DATABASE
USER
TASK
TOOL
ENVIRONMENT
CREATE COLLECT RECORD
The meaning can and quality will change across data creation, collecting and recording phases!
CREATE COLLECT RECORD
These are often missing!
The meaning can and quality will change across user interface, application logic and databases!
User
Interfac
e
Application
Logic
Data
Base
These are often missing!
Open Data and APIs can become ambiguous blackboxes due to unknown or hidden context factors
21
USER INTERFACE
APPLICATION
DATABASE
USER
TASK
TOOL
ENVIRONMENT
CREATE COLLECT RECORD
In the future, all context factors at Data Supply should be made transparent!
22
USER INTERFACE
APPLICATION
DATABASE
USER
TASK
TOOL
ENVIRONMENT
CREATE COLLECT RECORD
In practice, can you open the blackbox?
23
USER INTERFACE
APPLICATION
DATABASE
USER
TASK
TOOL
ENVIRONMENT
CREATE COLLECT RECORD
?
Implications for Open Data and Open API approaches
Future Research
Design Science Research to build better provenance support to technical and managerial methods.
Required provenance metadata is often unavailable!
Quantitative evaluations of Open Data products or Open Data interfaces.
There is need for constructs, models, methods and instantiations for Transparent Data Supply
ECIS 2015 ICIQ 2015
Are you interested?
Thanks for your attention!
QUESTIONS?
Sami Laine
Aalto University, Department of Computer Science and Engineering, Finland [email protected]
https://www.researchgate.net/profile/Sami_Laine/
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