Table 2 Guide to Addressing Obstacles in CDSS Development

Planning

Identify ideal source population

Too narrow a target population

Inappropriate selection of outcome measures

Involve a diverse group of stakeholders with different perspectives to identify shortcomings and goals

Involve diverse stakeholders to plan data collection

Inherent bias in data

Limited number of variables included in the data source

Limited availability of relevant data elements (e.g., Social Determinants of Health)

Poor quality of data dictionary

Produce data cards

Identify non-traditional sources of data

Improve data dictionaries

Ongoing construction of large, diverse, bias-aware, multimodal datasets

Data source has limited diversity and marginally relevant to target population

Limited number of large databases available

Poor choice of source data

Identify additional data sources

Propensity matching

Variability in source data content and formatting

Homegrown, narrowly focused data dictionaries and case report forms

No concern for reuse

Post-hoc harmonization of data

Adoption of a standard common data model for harmonization

Data

Data source identification

Existing data not appropriate for research question

Data is difficult to find

Use and modify data that can be found

Large data distribution platforms

Data access

Unclear governance

Data may be impossible to access

Data use agreements

Document investigators’ qualifications, giving tiered access to data

Transparent and uniform governance

Privacy

Heterogeneity of privacy rules

Technical issues with data sharing

Lack of guidelines

Awareness of regional privacy requirements

Adoption of sufficient technical solution to fulfill these requirements

Contract technical expertise

Democratized low-cost technical solutions

Data quality

Poor planning

Poor collection methodology

Data imputation

Limit inference

Adoption of existing tools

Adoption of guidelines on data quality assessment

Develop guidelines for emerging domains of data

Data Reuse

Limited use of common data models

Post-hoc harmonization

Promote easy access

Abide by FAIR principles (www.go-fair.org)

Model development

Technical performance

Choice of performance metric inappropriate for intended CDSS use

Risk of over-fitting and lack of generalizability

Model cards

Follow existing guidelines for reporting AI model performance

Good AI methodology stewardship

Algorithmic bias

Inherent or pre-processing bias in training dataset

Possible bias not assessed

Tools for bias assessment may be incomplete

Use available tools to assess bias at different stages

Tune models on subgroups on interest

Guidelines on multi-stage bias assessment

Disseminate known limits, shortcomings of CDSS

Trust and Explainability

Lack of trust in black box models

Poor performance of explainable models

Output not seen as useful or valuable by end-users

Expanding sets of methods to explain models

Chose explainable models whenever decrease in performance not clinically relevant

Engage end-users very early in the CDSS development

Team development from initiation to completion of model development

Prospective evaluation

Technical access to relevant real-time data

Governance barriers to real-time data

Latency in accessing real-time data

Simulate real-time data arrival

Silent deployment

Real-time EHR integration for deployment in a learning healthcare system

CDSS

Real-time benchmarking

Technical obstacles to deliver model prediction to bedside

Governance-based obstacles to bedside deployment

Ethical issues with real-time deployment

Early involvement of key administrative, technical, and clinical stakeholders

Flexible pipeline for scalable expansion of tools

Improved technical standards for real-time integration of AI applications with EHR vendors

Wide adoption of such standards

Transitioning a model into a CDSS

Human factor engineering

Implementation

Involvement of end-users in system design

Choosing appropriate metrics of performance

System usability and technology acceptance evaluations

Systematic usability and acceptability assessments

Periodic reassessments

Effectiveness assessment

Measuring impact

Impact on clinical outcomes

Impact on ICU processes

Impact on technical team

Choosing appropriate clinical outcomes

Measure resource use

Choosing appropriate technical outcomes

Engage stakeholder to define a process for technology effectiveness assessments

National guidance for technology effectiveness assessment

Ongoing compliance assessment

Adoption

Compliance

Identify internal “super users” to lead adoption

Involve end-users in evaluation

Unclear return on investment

Impact of economic outcomes

Engage financial decision makers early in system design

Collect data that allows economic assessment

Real-world deployment

CDSS degrading performance over time

Patient-specific issues changing or care giver support decreasing

Review model performance and address changes in input variables

Continual CDSS model performance evaluation

Modify CDSS to be more ergonomic for caregivers

Re-educate caregivers

Commercial availability

Many solutions are homegrown

Implementation gap and lack of practical solutions

FDA, GDPR clearance

Clinicians’ involvement in product development and testing

AI-technology assessment bodies in Health system that prioritize development

Evolving national guidance on product development and commercial clearance

Inherent cyber security of EHR-linked algorithms used in modeling

All phases

Limited technical expertise

Inherent lack of expertise of clinicians in big data manipulation and model development

Siloed development

Build interdisciplinary teams

Broadly available educational opportunities

Basic training in AI in clinical curricula

Early engagement of potential stakeholders

Worldwide datathons

Limited scalability

Solution not applicable outside ICUs or in resource limited environments

Conceive of critical illness as a continuum and formulate outcomes accordingly

Prioritize low-technology solutions