| Submitted By: Michelle Dougherty
/ Submitted on behalf of the CMS Data Element Library (DEL) Health IT Workgroup
|
| Data Element Information |
| Use Case Description(s) |
| Use Case Description |
Exchange of Data for Transitions of Care and Care Coordination:
Data exchange between acute care, post-acute care (PAC) providers, the patient and caregivers, and other care settings (physician practices, community based services, etc.), using common standards and definitions, supports patient-centered care by providing access to longitudinal information at transition and facilitating care coordination. An ASPE and CMS study found that 45% of Medicare patients required PAC services after an acute hospital stay.* The importance of exchanging data elements related to functioning at transition of care is described in the findings of the 2020 national study on hospital adoption of electronic health record functionality to support age-friendly care.** It concluded that US acute care hospital EHRs lack key features and health information exchange of important information that supports care for older adults, including structured documentation on mentation and mobility. Emerging work on frailty indices have found relationships between poor physical functioning and risk of adverse health outcomes.*** An ASPE funded project is currently exploring how health systems may use EHR-based data for frailty algorithms, including aspects of frailty related to mobility and cognitive limitations. Having functional limitations does not necessarily mean that a person is frail. Widespread exchange of the content specified in this data class (and corresponding data elements) proposal has the potential to improve patient and provider communications and supports access to longitudinal information that enables improved efficiencies, improved quality of care, and improved health outcomes.
The 2014 Improving Medicare Post-Acute Care Transformation (IMPACT) Act required the standardization and interoperability of specific categories of PAC patient assessment content, including the content applicable to the proposed data elements. For PAC settings including SNFs, HHAs, and specialty hospitals (inpatient rehabilitation facilities (IRFs), long term care hospitals (LTCH)), standardized patient assessment data reported to the Centers for Medicare and Medicaid Services (CMS) includes items about mental function, mobility, self-care and domestic life/IADLs that represented as observations. All of these concepts are represented in LOINC. The conceptual framework of the International Classification of Functioning (ICF) informed the development of these and other functioning data elements. Prior to use on the patient assessment, these data elements were tested for validity and reliability and are used for quality measurement, payment, public reporting and oversight.
Representation of functioning observations is well supported by all of the common exchange paradigms, including messages, documents, and APIs. Here we highlight the two most prevalent.
• FHIR: The HL7 PACIO project is focused on advancing interoperable health data exchange between PAC and other providers, patients, and key stakeholders across health care. The project has developed a FHIR Functional Status Implementation Guide and a FHIR Cognitive Status Implementation Guide (IG) which will be balloted as a standard for trial use in October 2020. The IG leverages the FHIR exchange structures and LOINC-coded observations. Successful exchange of functioning data from PAC assessments using the PACIO IGs was demonstrated in the January, May, and September 2020 Connectathons. PAC assessment data about mental function, mobility, self-care, and domestic life/IADLs are also included in use cases for the eLTSS and Care Plan HL7 FHIR projects.
• C-CDA: This data class and data elements are aligned with C-CDA templates for mental status and functional status. Functioning information from SNF and HHA patient assessments are being exchanged with HIEs and other providers using HL7 C-CDA. This exchange is supported by select EHR vendors, VorroConnect KeyHIE Transform, and the 360X data exchange initiative.
Sources:
*RTI International analysis of 2014 Medicare claims under contract with the Assistant secretary for Planning and Evaluation, August 2018, unpublished
**Hospital adoption of electronic health record functions to support age-friendly care: results from a national study. JAMIA 08/2020. https://pubmed.ncbi.nlm.nih.gov/32772089/
*** Validation of a Claims-Based Frailty Index Against Physician Performance and Adverse Health Outcomes in Health and Retirement Study. J Gerontol A Biol Sci Med Sci, 2019, Vol. 74, No. 8, 1271–1276
- VorroConnect: https://vorroconnect.com/products/healthcare-information-exchange/
- 360X: https://oncprojectracking.healthit.gov/wiki/display/TechLab360X/360X+Home |
| Estimate the breadth of applicability of the use case(s) for this data element
|
Stakeholder estimates can be challenging since the proposed data elements should be usable across the continuum of care, and beyond the traditional healthcare system – into the community. We know that PAC providers are required to collect standardized and interoperable data elements related to functioning at patient admission, discharge, and specified time intervals using defined CMS assessments. However, standardized capture of data elements related to functioning is inconsistent in other healthcare settings such as acute care hospitals and primary care/specialty care physician practices.
In attempting to quantify stakeholders potentially involved in the use and exchange of data elements related to functioning, we relied on the July 2020 CMS Fast Facts to provide a picture of both providers and patients (i.e., beneficiaries), albeit from a Medicare perspective. While the numbers that appear below are significant, it is important to remember that they are constrained to Medicare providers and persons served who are covered by original Medicare. Non-Medicare providers and patients who are not covered by original Medicare (e.g., Medicaid only, privately insured, etc.) are not reflected in the counts but would still be stakeholders for the proposed data elements. It is also important to note that caregivers and providers of long term supports and services (e.g., home health aides, meals on wheels) are increasingly recognized as important stakeholders in health information exchange but are also not reflected in the metrics below.
Institutional Medicare Providers (for 2019) & Persons Served (original Medicare for 2018)
• Inpatient Hospitals - 6,023 providers; 6.5 million persons served
• SNFs - 15,103 providers; 1.7 million persons served
• HHAs – 11,157 providers; 3.6 million persons served (Medicare Part A skilled and Medicare Part B non-skilled services)
Notes:
• The CMS Fast Facts reports include the 306 IRF providers and 367 LTCH providers, who are subject to CMS PAC assessment provisions, in the “inpatient hospital” counts for both providers and persons served. There is no setting specific count of persons served for these IRF and LTCH providers.
• Other types of Medicare Institutional providers were identified that would be stakeholders for data elements related to functioning, but for which separate counts of persons served were not available. There were 21,000+ providers identified as outpatient physical therapy/speech pathology, rural health clinics, federally qualified health centers, comprehensive outpatient rehab facilities, or hospice providers.
Non-institutional Medicare Providers (for 2019)
• Primary care, surgical specialties, medical specialties, and psychiatry – 537,390 providers
• Non-physician practitioners – 489,765 providers
Note:
• The non-physician practitioner classification (e.g., nurse practitioners, physician assistants) does not provide sufficient detail to discern the numbers of these practitioners associated with the targeted primary care and specialty providers.
Source:
• CMS Fast Facts July 2020 located at https://www.cms.gov/Research-Statistics-Data-and-Systems/Statistics-Trends-and-Reports/CMS-Fast-Facts/Downloads/CMS_Fast_Facts.zip
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| Link to use case project page |
https://confluence.hl7.org/display/PC/PACIO+Project |
| Use Case Description |
Support Quality Measures:
Standardized and interoperable PAC functioning data elements have the potential to be utilized to support clinical quality measures (CQMs) in a variety of existing quality reporting programs and future digital quality measures.
• Mental Status: As cognitive function can impact patient safety, as well as overall physical functioning, the data elements could be used for a quality measure that is focused specifically on cognition but may also be relevant to the construct of other eCQMs. Currently, CMS149v8, Dementia: Cognitive Assessment, has been validated for use with nursing home patients only using the Minimum Data Set (MDS) Brief Interview of Mental Status (BIMS).
• Mobility, Self-care, Domestic Life/IADLs: Dependence on others for ADL assistance can lead patient’s to feelings of helplessness, isolation, diminished self-worth, and loss of control over one’s destiny. As inactivity increases, complications such as pressure ulcers, falls, contractures, depression, and muscle wasting may occur.
• Functioning data elements could be used for a quality measure that is focused specifically on this data element (e.g., MIPS CQM #282: Dementia: Functional Status Assessment), and may also be relevant to the construct of other eCQMs. The Clinical Reasoning and PACIO FHIR initiatives have used PAC data in the May and September 2020 Connectathon demonstrations of CMS149 and CMS104.
Currently, there are six NQF endorsed measures that address concepts of functioning (NQF #2631 thru 2636) and use standardized data elements related to functioning from PAC assessments. One or more of these NQF measures are a component of the CMS Quality Reporting Programs (QRPs) for SNFs, IRFs, LTCHs, and HHAs. Additionally, data related to functioning are used in quality measures that help inform facility ratings in the Five Star Quality Rating System whose results are made available on CMS public reporting websites.
Sources:
• Clinical Reasoning FHIR Module: http://www.hl7.org/fhir/clinicalreasoning-module.html
• eCQM CMS149v8, Dementia: Cognitive Assessment: https://ecqi.healthit.gov/ecqm/ep/2020/cms149v8
• MIPS CQM #282: Dementia: Functional Status Assessment: https://qpp.cms.gov/docs/QPP_quality_measure_specifications/CQM-Measures/2020_Measure_282_MIPSCQM_v4.1.pdf
|
| Estimate the breadth of applicability of the use case(s) for this data element
|
Stakeholder estimates can be challenging since the proposed data elements should be usable across the continuum of care, and beyond the traditional healthcare system – into the community. We know that PAC providers are required to collect standardized and interoperable data elements related to functioning at patient admission, discharge, and specified time intervals using defined CMS assessments. However, standardized capture of data elements related to functioning is inconsistent in other healthcare settings such as acute care hospitals and primary care/specialty care physician practices.
In attempting to quantify stakeholders potentially involved in the use and exchange of data elements related to functioning, we relied on the July 2020 CMS Fast Facts to provide a picture of both providers and patients (i.e., beneficiaries), albeit from a Medicare perspective. While the numbers that appear below are significant, it is important to remember that they are constrained to Medicare providers and original Medicare persons served. Non-Medicare providers and patients who are not covered by original Medicare (e.g., Medicaid only, privately insured, etc.) are not reflected in the counts but would still be stakeholders for the proposed data elements. It is also important to note that caregivers and providers of long term supports and services (e.g., home health aides, meals on wheels) are increasingly recognized as important stakeholders in health information exchange but are also not reflected in the metrics below.
Institutional Medicare Providers (for 2019) & Persons Served (original Medicare for 2018)
• Inpatient Hospitals - 6,023 providers; 6.5 million persons served
• SNFs - 15,103 providers; 1.7 million persons served
• HHAs – 11,157 providers; 3.6 million persons served (Medicare Part A skilled and Medicare Part B non-skilled services)
Notes:
• The CMS Fast Facts reports include the 306 IRF providers and 367 LTCH providers, who are subject to CMS PAC assessment provisions, in the “inpatient hospital” counts for both providers and persons served. There is no separate count of persons served for these IRF and LTCH providers.
• Other types of Medicare Institutional providers were identified that would be stakeholders for data elements related to functioning, but for which separate counts of persons served were not available. There were a were 21,000+ providers identified as outpatient physical therapy/speech pathology, rural health clinics, federally qualified health centers, comprehensive outpatient rehab facilities, or hospice providers.
Non-institutional Medicare Providers (for 2019)
• Primary care, surgical specialties, medical specialties, and psychiatry – 537,390 providers
• Non-physician practitioners – 489,765 providers
Note:
• The non-physician practitioner classification (e.g., nurse practitioners, physician assistants) does not provide sufficient detail to discern the numbers of these practitioners associated with the targeted primary care and specialty providers.
Source:
• CMS Fast Facts July 2020 located at https://www.cms.gov/Research-Statistics-Data-and-Systems/Statistics-Trends-and-Reports/CMS-Fast-Facts/Downloads/CMS_Fast_Facts.zip
|
| Link to use case project page |
http://www.hl7.org/fhir/clinicalreasoning-quality-reporting.html |
| Healthcare Aims |
- Improving patient experience of care (quality and/or satisfaction)
- Improving the health of populations
- Reducing the cost of care
- Improving provider experience of care
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| Maturity of Use and Technical Specifications for Data Element |
| Applicable Standard(s) |
LOINC.
LOINC is a freely available global standard that contains a well-developed model for representing variables, answer lists, and the collections that contain them.* Many clinical assessments, scales, and other observations related to functioning are present in LOINC, including all of the variables on the PAC assessments for SNFs, IRFs, LTCHs, and HHAs. Regenstrief operates a robust process for adding new content (including functioning assessment instruments) if key gaps are identified.
*PMID: 22899966
https://loinc.org/panels/category/government/centers-for-medicare-and-medicaid-services-cms/
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| Additional Specifications |
PACIO Project: FHIR Cognitive Status Implementation Guide (STU ballot scheduled for October 2020)
PACIO Project: FHIR Functional Status Implementation Guide (STU ballot scheduled for October 2020)
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| Current Use |
In limited use in test environments only |
| Extent of exchange
|
N/A |
| Potential Challenges |
| Restrictions on Standardization (e.g. proprietary code) |
We are not aware of restrictions on standardization of proposed data elements in PAC settings. However, we are not aware of standardization of these proposed data elements in other care settings such as acute care hospitals, physician practices, etc. |
| Restrictions on Use (e.g. licensing, user fees) |
Intellectual property issues related to use of the BIMS, CAM, and PHQ2/PHQ9 in PAC assessments have been addressed through appropriate annotations on printed and electronic representation of these items. as proposed data elements have not been standardized in acute care settings, assessments of functioning used in these settings would need to be evaluated for intellectual property considerations. |
| Privacy and Security Concerns |
This data should be exchanged securely. Secure data transfer process as governed by CMS and ONC should be followed |
| Estimate of Overall Burden |
PAC settings such as SNFs, IRFs, LTCH, and HHAs, are required to report standardized patient assessment data to CMS for purposes of Medicare payment, quality reporting, and compliance surveys. These patient assessments include concepts of mental function, mobility, self-care and domestic life/IADLs.
Outside of the post-acute care settings, these data elements may not be structured and are not captured with consistency.
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| Other Implementation Challenges |
For PAC settings, the proposed data elements are collected from standardized assessments and reported to CMS using agency specific data submission protocols and specifications. However, as PAC providers are excluded from EHR certification requirements, there is significant variance in the adoption of interoperability terminologies and exchange standards by EHR vendors supporting post-acute care settings. Additionally, while some providers may capture mental status, mobility, self-care and/or domestic life/IADL data in acute care or outpatient settings, they may use other instruments (observations) to capture this data. |
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Submitted by rdillaire on
CMS-CCSQ Recommends the advancement of Self-care to Level 2
Recommendation: CMS CCSQ recommends the Self-care element be advanced to Level 2.
Rationale: Including the Self-care data element at Level 2 is crucial for improving patient care and outcomes in the LTPAC setting. Self-care is a broad domain encompassing activities like eating, dressing, and personal hygiene, which are essential aspects of a patient’s functional status. By tracking self-care abilities, healthcare providers gain valuable insights into a patient’s progress, identifying any barriers to independence and enabling tailored interventions that promote recovery and independence. This data directly impacts the management of chronic conditions and recovery after illness or surgery. Self-care data is already captured and exchanged in standardized functional assessments, such as Section GG in the IRF-PAI, LCDS, MDS, and Outcome and Assessment Information Set (OASIS), and supports tracking functional outcomes over time. It is also aligned with national healthcare initiatives, including CMS Quality Reporting Programs (QRPs), improving the accuracy of reporting for quality measurement and reimbursement purposes. Given its direct relevance to patient care and healthcare quality metrics, advancing Self-care to Level 2 will signal the importance of capturing this data element in healthcare IT systems to vendors which may increase its adoption and facilitate better care coordination, improve patient outcomes, and enhance interoperability across healthcare systems. The inclusion of Self-care in Level 2 would support standardized observation data exchange, as demonstrated in PACIO projects like the Personal Functioning and Engagement (PFE) Implementation Guide (IG) and Cognitive Status IG, further enhancing its utility across diverse care settings.