[Solution]Technology & Infrastructure

slide presentation of 12 with note in the note section , 5 references, apa7 d make your recommendations to the organization. . ( use the…

slide presentation of 12 with note in the note section , 5 references, apa7
d make your recommendations to the organization. . ( use the Analytics Strategy Template to check your work.)
10. Technology & Infrastructure (Be as detailed as possible and use the Analytics Strategy Template to check your work.)

Is the current technological system sufficient to implement your proposed plan?
Are there any issues with the infrastructure that need to be addressed?
How will the data be managed?
What is the quality control process?
How will data be integrated into the larger organization?
Consider the economics and costs of the new system

11. Future State

Address any gaps that you have discovered.
Make recommendations to the organization to address the gaps.
What reformation needs to take place in order to deliver better healthcare?
What changes do you expect to take place if the system is implemented?

12. Communication Plan

Describe your plans for communicating the data analysis and plan to all the stakeholders.
Including a graph or chart may help you present your findings.
Explain the connection between your findings, your proposal, and executing the organization’s strategy.

This is the conclusion so you need to make your recommendations to the healthcare system or organization.

Stakeholders and users A stakeholder is a person (or group of persons) that are: impacted by, users of, or otherwise have a concern (or interest in) the development and deployment of analytical solutions throughout the healthcare organization.
When developing an analytics strategy, it is important to understand what each of the likely analytics stakeholders will require, and develop approaches to ensure they are getting what they need.
Stakeholder summary
Organization structure
Use cases
Clearly identify who the key stakeholders are in the analytics strategy. Major stakeholder groups in healthcare include: Patient – The person whose health and healthcare
experience we’re trying to improve with the use of analytics Sponsor – The person(s) who supports and provides
financial resources for the development and implementation of the analytics infrastructure
Influencer – A person who holders considerable influence over the support of analytics initiatives.
User – A person in the organization who accesses analytical tools, or uses the output of analytical tools, to support decision making and to drive action.
To what extent are the stakeholders currently engaged in developing the strategy and/or the use of analytics within the healthcare organization?
Reference Appendix A for a stakeholder analysis form you can use to capture key details.
Where are stakeholders located within the organization? (attach an organization chart, if available and applicable.)
Obtaining as much information as possible about the possible uses of analytics will help to: identify any gaps in analytics capabilities, and reduce the likelihood that critical analytics needs will be missed.
Apply analytics use cases to help identify: what data elements are most important, what indicators will be necessary to calculate, and what types of usability factors (such as dashboards, alerts,
and mobile access) need to be considered. decisions for which analytics insight is required actions that get triggered by analytics indicators risks that analytics identify and/or help to mitigate what key processes need to be monitored and/or improved what indicators are required to monitor quality and
performance Use the Analytics Use Case document available in the
downloads section of http:HealthcareAnalyticsBook.com to document key analytics use cases from your stakeholders.
Provide a summary of the most important (or highest priority) use cases in the strategy document.
Other considerations to document, where appropriate, include: Who is using the existing analytics tools within the

What is the level of stakeholders’ analytical sophistication (For example, do the “dabble” in Excel, or are they expert statisticians?)
How are analytics tools being used? (For example, are analytics being used primarily for operations, or research, or are analytics being used at the clinical point-of-care?)
What business, quality, and/or clinical questions are being answered? Perhaps, more importantly, which ones are NOT being answered due to limitations in current analytic capability?)
How can stakeholders’ use of (or access to) data, analytics, and overall business and clinical insight be improved?
Process and Data An analytics strategy must address: how to determine which data is most necessary for quality and performance improvement, how the data is managed and its quality assured, and how data links back to business processes for necessary context.
Data sources
Data quality
Data governance & stewardship
Data is the raw material of analytics, and data and processes are very closely linked. It is the business processes and clinical workflows that generate data, and these same processes and workflows must be well understood to provide critical context to data for analysis.
What are the sources of data in the organization? What operational and clinical systems contribute to source data?
Are all necessary data sources available for analytics? Are these data sources linked (where possible) and integrated into a data store suitable for analytics such as an Enterprise Data Warehouse (EDW)?
What data is necessary from source to develop analytics that address key business issues?
How good is the quality of available data? What are possible/likely sources of data quality issues?
How well do the source systems enable high-quality data input (i.e., through extensive input validation, etc.)
What gaps in data exist? Do gaps in data exist because data is not collected in source systems, or because it’s not integrated into accessible data stores?
Is there a culture of strong data stewardship and governance within your organization? What policies and procedures exist for governing data?
Who is responsible for: Monitoring and evaluating data quality, identifying issues,
and making appropriate recommendations to fix data quality issues?
Ensuring that any modifications to data storage and management are in line with accepted policies and procedures.
Ensuring that data is used properly and that it is accessible to those who need to use it.

Business processes
Data model
Gap Analysis
Helping to establish enterprise-wide standards for data quality and usage.
Most quality improvement methodologies monitor progress and evaluate performance and outcomes using indicators based on process data.
This requires a strong alignment between key business processes and the data that measures those processes.
As part of the analytics strategy, you should consider: if and how current business processes are documented, and how data items are mapped to these documented business
processes. whether any formal business process management (BPM)
tools are utilized to catalogue processes, workflows, and their associated data points.
Data modelling helps to identify the many sources of data, and understand how interconnected data is. The model can also highlight the potential uses of data within a healthcare organization by showing connections between data sets that may exist beyond department or program boundaries.
If available, attach a high-level data model illustrating key information sources within your healthcare organization.
Clearly outline the gaps in data sources, and in how data is managed within the organization. Prioritize all gaps in the understanding, acquisition, and management of both data and processes knowledge so high-priority gaps can be addressed quickly. For example, if your organization does not have a formal data model, you might consider having a data architect construct a “current state” data model, then examine opportunities for enhancements in the data model.
Tools and Techniques
Analytics needs
Application types
Analytical tools must meet the requirements of analysts building analytics solutions/applications, and the end-users who will rely on the resultant information and insight.
It is important to align analytics tools, methods, and capabilities with: Business and quality questions that need to answered Relevant quality goals and Key Performance Indicators
(KPIs) Data available Stakeholder requirements and analytical sophistication Appropriate statistical analyses Tools/software available
Conduct an inventory of existing analytics tools to determine if:

Gap analysis
Capability is missing that will be required (for example, tools that enable predictive modelling)
Existing capabilities exist that may not be widely known (for example, pockets of expertise in the use of specialized tools for simulation, and other advanced analytics)
Different types of analytics tools that may be in use within the organization include: Statistical – Used for deeper statistical analysis not available
in “standard” business intelligence or reporting packages Data visualization – Used for developing interactive, dynamic
data visualizations that aid with analysis Data profiling – Helps to understand and improve the quality
of an organization’s data. Data mining -Analysis of large data sets to uncover unknown
or unsuspected relationships. Text mining – Analysis of unstructured, text-based data to
extract high-quality information. OLAP -Allows analysts to interactively explore data by
drilling-down, rolling up, or “slicing and dicing” data.
Do the analytics teams in the organization have all the necessary tools to accomplish the jobs they are tasked with?
What tools are required that would be more appropriate or more efficient for the types of analytics required?
Identify viable best-of-breed vendor solutions that meet requirements; custom-build from scratch if necessary (or if participating in research).
Team and Training PEOPLE are, by far, the most important consideration when developing an analytics infrastructure
Organizational considerations
Gap analysis
An analytics strategy must consider: What kinds of people (and the skills they bring) are
necessary How to attract the best analytical talent How to retain the analytic talent within your HCO
Different resource management models exist for analytics: “centralized” analytics office (most or all analysts reside in a
central office providing) “distributed” analytics resources (most or all analysts reside
within departments and/or programs) “virtual” center of excellence / competency center
(many/most analysts reside within various departments and/or programs, but a core or “home base” exists to provide a common set of standards, training, and tools regardless when they sit within the organization).
Do we have enough of the right types of people? Where do analytics professionals reside? To whom do they report?

Future state
What support is available for analysts? What support do they need?
I.e., single voice of a distributed analyst group How are they trained, and what training opportunities are
available? What are the standard hiring and performance requirements? What key skills are required and need to be added to the
analytics team? What types of professionals can best provide these skills to the team?
What training or skills development needs do the team have? Is the team adequately staffed with the right people?
Once the strategic goals are addressed, what will the analytics team look like with respect to: Number of resources adequate to do the job Composition (types of people based on education
background and related work experience) Roles (Are the right people doing the right time of work? Are
people going to be more generalists and taking on multiple types of roles, or will people be more specialized)
How can the organizational structure (relating to location and support of analytical professionals) be structured (or improved) to better meet the professional needs of analysts, and better meet the analytical needs of the organization?
Technology & Infrastructure Ideally, the analytical needs of an organization and the technological requirements to achieve those needs will figure prominently in the organization’s analytics and IT infrastructure deployment strategy.
Current state
Data management
Document currently available technology and infrastructure that support analytics. This includes application and database servers.
More importantly, document how well current infrastructure (networks, servers, and storage) copes with analytic demand, and describe any performance issues or other gaps that may be limiting analytic capability and usefulness. Examples of things to consider include: Overall performance (how long do reports and other queries
take to run) Reliability (how often do downtimes of infrastructure occur)
What current data management systems are in place, and how well are the systems meeting current demand for analytics. Data management systems to consider include: Data Warehouses (DW) Operational Data Stores (ODS) Data Marts (DM) General storage and backup

Gap analysis
What systems are in place to enable data integration? Are these systems sufficient for current (and anticipated future) requirements? Extraction / Load / Transformation (ETL) Data Quality (DQ) (cleansing, profiling, management) Service Oriented Architecture (SOA) Business Event Monitoring (BEM) Complex Event Processing (CEP) Business Process Management (BPM) Business Rules Engine (BRE) Enterprise Information Integration (EII)
What gaps exist in the current technology and infrastructure that constrain analytics within the organization. Considerations include: Are reports, queries, and other tools taking too long to
execute? Are data integration (i.e., ETL) services validated for data
quality, and optimized for best performance? Do servers have sufficient memory? Are server and/or network reliability issues causing
downtimes or other issues that are affecting the ability to make accurate and timely decisions in the organization?
1. Future State Gap analysis
Next Steps
Summarize all the gaps identified in previous sections.
Prioritize the gaps based on importance/impact to the organization and the effort required to resolve the gap and/or mitigated risks associated with the gaps. Aim for high-impact and low to medium effort
projects initially to achieve early “wins” and build on success.
Use the Gap Analysis Form available in the downloads section of http:HealthcareAnalyticsBook.com to help construct your strategy’s gap analysis.
What options have been considered for strategic change and on what basis have the decisions been made on which to progress?
What are your organization’s priorities for addressing identified gaps?
Who is responsible for undertaking work to address specific gaps? Will this take away from “regular” work and responsibilities? How will this impact overall performance of the analytics and IT teams?

What timeframes is the work expected to be performed in? One to three years is a typical timeframe to
consider. Planning too far ahead leads to limited returns because of changing technology and changing needs of the healthcare organization.
When all important gaps have been addressed, will the strategic vision described earlier be achieved?
How do we expect the key performance indicators and outcomes to change as we implement ?
Note: This section should NOT include a detailed project plan to achieve each of the identified strategic priorities. Once the overall strategy is approved, detailed project plans (where necessary) can be drawn up.
2. Communication plan
Who will this strategy be communicated to?
How will this strategy be communicated to stakeholders?
Plan for communicating the content of the strategy, and ongoing progress, to all stakeholder groups (taking into account differing needs/ expectations/ levels of involvement)
How do stakeholders expect or prefer to be notified when this strategy document is released (or updated)? Different preferences may include: Full document format Abridged / executive summary Slide stack (i.e., Powerpoint) file In-person overview Presentation
How often does each stakeholder (or stakeholder group) need to be notified of updates to the strategy?
In all likelihood, multiple approaches will need to be employed to ensure that all stakeholders are kept appraised of the analytics strategy release and update cycle.

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