B08_SOL_-_Attachment_8_-_Sample_Task_Order_Descriptions.docx

Sample Task Orders Descriptions

Functional Area 1 Sample:

Background/Purpose Adult protective services (APS) programs are state-run programs that are not subject to federal rules and regulations. As a result, each state has designed its own unique system. In addition, there is no single funding stream for APS, forcing states to look to multiple programs for funding and often leaving states with inadequate resources and budget reductions for their APS programs. Yet, data from state APS agencies shows an increasing trend in reports of maltreatment and increasing caseloads for APS workers. All of these challenges can cause significant barriers for responding in an effective and timely way to reports of elder abuse, neglect, and exploitation.

Thus, it is more important than ever to show the effectiveness of APS and the ability of these programs to improve client outcomes. Establishing the evidence-based for APS programs and practices, promoting the use of evidence-based and promising practices, and developing guiding standards is key for showing the benefit of APS programs to help advocate for funding and legislative support.

The first and only study that examined the outcomes of older adults who were abused and interacted with APS and compared them to the outcomes of older adults who were abused and did not interact with APS, was done in 1968. The results of that study showed that intervention by APS led to higher rates of guardianship, institutionalization, and death. However, the APS programs of 1968 were not informed by today’s emphasis on least-restrictive environment, person-centered care, or supported decision-making.

The purpose of this task is to design and conduct a study that examines the question: “How does APS make a difference in the lives of older adults and adults with disabilities who interact with it today?”

Scope of Work This project entails the following activities:

• Conduct a literature review of client outcome evaluations focused on abuse and neglect from relevant APS and Child Welfare literature, including peer-reviewed articles, available reports, and documentation on ACL’s APS process evaluation;

· Identify and convene a technical expert panel to provide input on the design and conduct of an APS client outcomes study;

· Determine the research question(s) and client outcome(s) that should be the focus of this study;

· Expand the APS process logic model to incorporate client outcomes;

· Review existing data sources to determine if and what data are already available to answer the study’s research questions and examine APS client outcomes. If relevant, existing data are available, determine any requirements to access and use the data (e.g., application, data use agreement, financial costs).

· Design APS client outcomes study;

• Prepare for data collection:

· Obtain existing data and prepare data for analysis; and

· Develop and pilot data collection procedures and instruments; engage IRB and submit materials for IRB review; prepare PRA application package for OMB;

· Identify research location(s) and, if necessary, establish agreements with research sites;

Functional Area 2 Sample:

Background A 2018 CMS, Center for Medicaid & CHIP Services (CMCS) Informational Bulletin entitled, “Health and Welfare of Home and Community Based Services Waiver Recipients,” discussed CMS’s commitment to prioritizing the health and welfare of individuals receiving Medicaid-funded Home and Community-Based Services.[footnoteRef:1] In this informational bulletin, CMS discussed several strategies that states should use to ensure the provision of quality HCBS, protect the health and welfare of Medicaid beneficiaries who receive HCBS including older adults and people with disabilities, and prevent future incidents of adult and elder abuse, neglect, and exploitation (maltreatment). Although it was not the focus of the informational bulletin, CMS and ACL recognize the potential value of, and have an interest in, exploring the use of predictive analytics as one component of a broader strategy to predict and prevent adult and elder maltreatment and, in doing so, protect the health and welfare of older adults and people with disabilities. [1: ]

While used extensively in other fields, predictive analytics is a relatively new concept for HCBS and for understanding interpersonal violence, particularly adult and elder maltreatment. Predictive analytics generally refers to the use of a range of statistical techniques such as predictive modeling and data mining that use current and historical data to make predictions about future events, and uses powerful and flexible technology tools, including, but not limited to, cloud computing and cloud data storage. In the human services field, child welfare programs have been an early candidate for predictive analytic approaches. While still relatively new, a number of states and counties have begun to invest in predictive analytic approaches to help prevent child abuse and neglect, particularly deaths and serious injury, as well as to avoid other negative outcomes associated with long-term involvement in the child welfare system.

Initiatives by other federal agencies have presented new opportunities for CMS and ACL to move forward in this area. In preparation for this requirement, ACL conducted an extensive literature review and environmental scan to understand the following:

· Prior and current use of predictive analytics, machine learning, and AI in other populations;

· Known/estimated risk and protective factors for adult and elder maltreatment;

· Value/strength of available social science, healthcare, criminal justice, and other databases and datasets for inclusion in the project.

Additionally, ACL has taken the 1st step to build a cloud-based environment to facilitate the application of machine learning, AI, and predictive analytic tools for creating an algorithm to predict risk of adult and elder maltreatment at the community-level using the information gathered in the above activities.

The work described in this PWS will build on this foundational work to develop a valid algorithm for predicting community-level, as well as individual-level, risk for adult and elder maltreatment. This project is intended to assist HHS in identifying opportunities for the federal government to facilitate data access and the use of the most effective methods for leveraging machine learning, AI, and social scientific data analysis while ensuring that potential pitfalls are understood and taken into account, such as inherent bias. In turn, understanding of risk and protective factors will improve interventions to prevent, or effectively intervene in, adult and elder maltreatment, and as an outcome, improve health quality outcomes and reduce health care expenses. This project’s specific objective is discussed in further detail under “Primary Objective.”

Primary Objective ACL seeks to leverage artificial intelligences, machine learning, and other “big data” tools to investigate data sets to determine if patterns exist in the data that indicate a correlation with reported incidence of elder abuse. The project will explore the development of predictive analytic tools and propose algorithms that could be used as part of a protocol to identify risk factors and potential abuse of older and vulnerable adults first at the community-level, and then, after validation, at the individual-level.

The experiment will use existing data sources, where possible, including ACL’s National Adult Maltreatment Reporting System (or, NAMRS), Medical Expenditure Panel Survey, National Health Interview Survey, Census data, and DOJ’s National Incident Based Reporting System, in addition to Medicaid HCBS and Medicare payment data. A component of the experiment will be to assess the possibilities for associating disparate data sets to infer variables such as social isolation, cognitive function, dementia, and other identified risk factors.

The project also requires developing tools using cloud computing resources and data storage that enable data analytics, artificial intelligence, and machine learning to be applied to the selected data sources. This work requires close collaboration among the data scientists, social science subject matter experts, and cloud computing architects, developers, and integrators involved with the project in order to apply the tools to the data sets, to analyze the results of the machine learning, and to propose algorithms that may allow risk factors to be applied to data to determine the probability of abuse, exploitation, and maltreatment of older and vulnerable adults at the community and individual levels.

Scope of Work ACL is seeking innovative and cost-effective approaches to address the objective listed in Section 2, above. ACL is seeking an entity with subject matter expertise on elder and adult maltreatment and data science/analytics, as well as leveraging innovative and cutting-edge technology to conduct data analysis. Specifically:

· Experience and expertise with undertaking research questions in field or adult and elder maltreatment;

· Demonstrated familiarity working with data sets of varying quality and completeness and skills/strategies to address gaps in data and increase confidence levels;

· Expertise in applying cutting-edge data science, data analytics, artificial intelligence, and machine learning to social scientific research questions.

Various social science, health care, and criminal justice data sets have been identified initially, and will be used, alone or in combination with other data sources (including state-level data sets), to identify older adults and people with disabilities at highest risk for maltreatment at (a) the community-level and (b) the individual –level. Requirements include:

1. Providing expert guidance to ACL and CMS on refining the research question(s);

2. Proposing and deploying cloud-based data analysis tools for the different phases of the project (community-level algorithm testing; individual-level algorithm development and subsequent testing);

3. Acquiring and preparing data sources for the application of machine learning, AI, and other predictive analytic tools;

4. Defining business rules and providing subject matter expertise that establish the valid uses of the data and reduce or eliminate inherent biases in the data;

5. Identifying and/or creating the software, code, and/or program appropriate for analyzing the data sets;

6. Developing and testing a predictive algorithm using risk terrain modeling, geo-spatial modeling, or other methodologies to identify (a) locations, and then (b) individuals at highest risk for maltreatment;

7. Validating the algorithm(s);

8. Supporting ACL in informing the project’s technical expert panel on project’s status, challenges incurred, and decision points;

9. Documenting the work above in a formal report format of professional quality.

Functional Area 3 Sample:

Background The purpose of this requirement is to secure services and solutions that meet the agency’s needs to communicate effectively to the public and stakeholders about ACL’s elder justice efforts broadly, and specifically about adult protective services program efforts. Communication initiatives about ACL’s elder justice activities and efforts should reflect ACL’s commitment to a person-centered approach that is based on people’s strengths, assets, goals, culture, and expectations, along with their needs, and is based on the belief that all individuals have the right to exercise choice in and control of the direction of the services they receive.

The ACL Office of Elder Justice and Adult Protective Services (ACL OEJAPS) is the ACL Program Office responsible for overseeing ACL’s APS initiatives.

Scope of Work

ACL seeks the best solutions for creating content and communications most relevant and important to ACL’s stakeholders about its elder justice work. The solution should reflect ACL’s commitment to a person-centered approach that is based on people’s strengths, assets, goals, culture, and expectations along with their needs, and that is based on the belief that all individuals have the right to exercise choice in, and control of, the direction of the services they receive. ACL is seeking solutions that challenge current assumptions and methods, and that propose new, innovative, and creative approaches and reasonable strategies to implement those approaches.

Functional Area 4:

Background

HHS policy on person-centered thinking, planning, and practice is articulated in statute, regulations, guidance documents, prior grant and contract actions, and numerous HHS sponsored presentations. The CMS HCBS final rule published in January 2014 and the Secretary’s Guidance for Implementing Standards for Person Centered Planning and Self-Direction issued in June 2014 provide the clearest HHS articulation to date on the expectations of state programs related to person-centered thinking, planning, and practice. The CMS rule on person-centered planning applies to 1915(c) waivers, and the 1915(i) and 1915(k) state plan options. The section 2402(a) Guidance targets all HHS programs serving people with disabilities and older adults.

The 2402(a) guidance has informed HHS efforts in several programs including the ACL No Wrong Door program, the SAMHSA Certified Community Mental Health Clinics, SAMHSA Mental Health Block Grant Program, the CMS Reform of Requirements for Long-Term Care Facilities, and the Office of the National Coordinator (ONC) eLTSS data elements.

Despite the progress in HHS policy and programming, state programs continue to grapple with how to effectively implement person-centered thinking, planning, and practices in a manner consistent with the policy intentions. HHS is consistently approached by state program officials for TA related to some aspect of person-centered thinking, planning, and practice implementation. Inquiries have included requests for operational definitions, how to reconfigure systems to support person-centered planning and service delivery, what training models are available and how to choose one most appropriate for a given state system, how to structure payment systems to support person-centered planning, how to select and implement structural, process, and outcome quality measures to effectively evaluate the impact person-centered planning has in state systems, etc.

While HHS makes every effort to address state issues as they arise, the lack of coordination across HHS, coupled with the high demand for dependable, actionable, and state specific TA has made it difficult to adequately address demand. This task order seeks to augment existing state efforts to implement HHS policy and guidance in HCBS programs by creating a National Center for Advancing Person-Centered Practices and Systems (NCAPPS) that acts as a central clearinghouse for all stakeholders to access useful information, to provide effective TA to states on the full spectrum of needs related to implementing person-centered thinking, planning, and practices in their systems, and to support a state-to-state learning community of practice to facilitate the development and sharing of best practices across state systems.

Competitive bids for this Task Order from contractors will demonstrate capacity in the following areas:

· Formalized and ongoing partnerships with people with disabilities and older adults in designing and implementing person-centered thinking, planning, and practice systems in state programs;

· Expertise and knowledge providing technical assistance on person-centered thinking, planning and practices for No Wrong Door Systems, HCBS waiver programs, and Traumatic Brain Injury (TBI) programs;

· Effectively seeking out, valuing and incorporating the perspectives of people with disabilities of all ages in designing, implementing and continuously improving person centered thinking, planning, policies and practices;

· Developing coordinated statewide approaches to person-centered thinking, planning, and practice systems change and organizational development in HCBS programs, NWD systems, and TBI programs;

· Promoting and enhancing community-based LTSS programs for all persons regardless of age, income or disability;

· Expertise in a number of person-centered thinking, planning and practice approaches;

· Providing state and local HCBS program developers with access to a comprehensive knowledge base to support the development of HCBS-LTSS programs;

· Facilitating peer-to-peer technical assistance and information sharing including E- Learning models;

· Providing off-site TA, including by telephone and e-mail, moderated and un-moderated listservs, webinars and topic-based conference calls;

· Offering on-site technical assistance, including virtual stakeholder meetings (local and national), assistance at the grantee’s site, and long-term virtual TA projects (weeks or months).

ACL seeks access to a “cadre of experts” available either directly from the contractor or indirectly (through subcontracts, etc). This cadre will include individuals that rely on paid and/or unpaid HCBS with lived experience in navigating systems. The experts should be available for written analyses, planning meetings with the ACL and CMS, and to render assistance as prescribed by the COR to reconfigure state HCBS systems, No Wrong Door systems, and Traumatic Brain Injury programs to be more consistent with the principles of person-centered thinking, planning, and practice.

Functional Area 5 Sample:

The Contractor shall work with the Administration for Community Living (ACL) to provide support for the virtual Older Adult Mental Health Awareness Day Symposium, scheduled for May 11, 2023.

Background

ACL, the Substance Abuse and Mental Health Services Administration (SAMHSA), and the Health Resources Services Administration (HRSA) developed the first Older Adult Mental Health Awareness Day in May of 2018. Subsequent events occurred in 2019, 2020, 2021, and 2022. The 2021 and 2022 events were co-funded by ACL, HRSA, and SAMHSA – with planning assistance from CMS, the VA and other national organizations. Since 2021, the ACL National CDSME Resource Center has coordinated the planning and day-of production of the event and will do so again in 2023. The event grew from approximately 900 participants in 2019 to over 3,800 participants in 2022. Speakers at the events have included clinicians, home and community-based service providers, advocates, peer support specialists, people with lived experiences, researchers, program administrators, and others.

Scope of Work

The Contractor shall work with ACL to support the 2023 Older Adult Mental Health Awareness Day (OAMHAD) Symposium. The Contractor shall meet weekly with the ACL appointed Contract Officer Representative to ensure milestones are achieved in a timely manner. The work will include securing a paid keynote speaker (identified and approved by ACL and the OAMHAD Executive Committee), providing American Sign Language (ASL) services for each of the sessions, providing written summaries of each of the sessions, and supporting miscellaneous allowable expenses associated with the ACL National CDSME Resource Center coordination of the event.