The single most important data set for improving home healthcare outcomes is the Outcome and Assessment Information Set, known as OASIS. Required by Medicare since 1999, OASIS is the standardized tool home health agencies use to collect clinical, functional, and demographic data on every adult patient. The current version, OASIS-E, took effect in January 2023 and feeds directly into quality measures that determine public ratings and reimbursement. But OASIS is just the foundation. Agencies that layer in remote monitoring data, claims-based measures, patient experience surveys, and medication reconciliation records see the clearest improvements in readmission rates, functional recovery, and patient safety.
OASIS: The Core Data Set
CMS requires every Medicare-certified home health agency to collect and transmit OASIS data for all adult patients reimbursed by Medicare and Medicaid (with narrow exceptions for patients under 18, maternity services, and housekeeping-only care). OASIS-E captures dozens of individual data items across several domains, and each one maps to a specific outcome measure that CMS tracks and publicly reports.
Functional status items are among the most consequential. These include data points for grooming ability, upper and lower body dressing, bathing, toilet transferring, bed transferring, and ambulation. Each item is scored at admission and again at discharge, producing a change score that reflects whether the patient improved, stabilized, or declined. Clinical items track breathing difficulty, bowel incontinence, cognitive functioning, and confusion frequency. Skin integrity items document pressure ulcer staging from Stage 2 through deep tissue injury. Together, these data elements form the backbone of every quality comparison published on CMS’s Care Compare website.
The practical value of OASIS data lies in what agencies do with it between assessments. Agencies that review OASIS trends in real time can identify patients who are plateauing in mobility or worsening in breathing and adjust care plans before a hospitalization becomes necessary. Agencies that treat OASIS as a billing checkbox, completing it only at required time points, miss the clinical intelligence it was designed to provide.
How OASIS Data Drives Reimbursement
Under the expanded Home Health Value-Based Purchasing (HHVBP) Model, agencies receive payment adjustments based on their performance across a specific set of quality measures, benchmarked against peer agencies nationwide. The measures fall into three categories, each drawing from a different data source.
OASIS-based measures currently include improvement in breathing difficulty, improvement in oral medication management, and composite scores for changes in self-care and mobility. Starting in 2026, CMS will add improvement in bathing, upper body dressing, and lower body dressing as separate measures. Claims-based measures track acute care hospitalization during the first 60 days of home health, emergency department visits without hospitalization, and potentially preventable hospitalizations. A third category uses HHCAHPS survey data, covering patient ratings of professional care, communication, team discussion of care issues, overall satisfaction, and willingness to recommend the agency.
An agency’s total performance score across all three categories determines whether its Medicare payments are adjusted upward or downward. This means the data an agency collects isn’t just clinical documentation; it’s the direct mechanism that sets revenue. Agencies that invest in accurate, timely data collection across OASIS, claims, and patient surveys consistently outperform those that don’t.
Remote Monitoring Data
Remote patient monitoring (RPM) adds a continuous data stream that fills the gaps between in-person visits. A systematic review in NPJ Digital Medicine found that RPM interventions improved mobility and functional status across multiple conditions, while reducing hospital admissions, readmissions, length of stay, outpatient visits, and non-hospitalization costs.
The evidence is strongest for heart failure. Patients monitored remotely with devices that transmitted blood pressure, heart rate, and body weight had fewer cardiovascular deaths and heart failure hospitalizations compared to those receiving usual care. Smartphone applications connected to Bluetooth blood pressure cuffs, scales, and fitness bands reduced 30-day readmissions. For patients with both COPD and heart failure, an integrated home-based telerehabilitation program that included remote cardiorespiratory monitoring produced measurably longer walking distances after four months.
The data types that matter most in RPM are daily weight trends (which catch fluid retention early in heart failure patients), blood pressure patterns, heart rate variability, and oxygen saturation levels. The key isn’t just collecting these numbers. It’s having a clinical team that reviews alerts and responds quickly. Studies that showed the greatest benefit paired automated monitoring technology with structured disease management programs and nurse follow-up calls.
Visit Frequency and Severity Scoring
How often a clinician visits a patient at home generates its own data set, and changes in visit frequency carry predictive power. Research published in Frontiers in Public Health found that patients whose visiting nurses increased the frequency of home visits, reflecting a worsening overall condition, were nearly seven times more likely to need emergency medical transport. When nurses also reported increased severity of illness, the odds jumped to nearly 13 times higher.
This finding works in both directions. Tracking visit frequency alongside a severity scoring system (typically graded on a scale from monthly to more frequent based on total symptom scores) helps agencies identify patients on a deteriorating trajectory before a crisis. It also helps with resource allocation: patients scoring in lower severity grades can safely receive less frequent visits, freeing clinical time for those at higher risk.
Medication Reconciliation Records
Medication errors are one of the leading causes of preventable hospitalizations in home health patients, and the data needed to prevent them is straightforward but often incomplete. Effective medication reconciliation requires a comprehensive list that goes beyond prescription drugs to include herbals, vitamins, nutritional supplements, over-the-counter medications, and any other substances the patient takes.
The reconciliation process compares what the patient is currently taking against what has been newly prescribed, checking for duplications, interactions, incorrect doses, and omitted medications. The critical data fields are drug name, dose, route, and frequency. Electronic prescribing systems capture these fields automatically, but the home setting introduces a complication: patients often have medications from multiple prescribers, leftover bottles from previous regimens, and supplements they don’t think to mention. Agencies that build thorough medication inventories at each admission and transition point catch discrepancies that would otherwise lead to adverse events.
Medication reconciliation is also one of the Standardized Patient Assessment Data Elements (SPADEs) that CMS is evaluating for use across all post-acute care settings. Other SPADEs categories include hearing, vision, cognition, pain, mood, care preferences, continence, and behavioral signs. The goal is to create a common data language so that when a patient moves from a hospital to a skilled nursing facility to home health, every provider works from the same baseline assessment.
Predictive Analytics and Combined Data
The most sophisticated home health programs combine multiple data sets into predictive models that flag patients likely to be readmitted. One well-studied algorithm, used within an integrated health system, predicts readmission risk using just five variables: a laboratory-based illness severity score computed at admission, a comorbidity score, length of the preceding hospital stay, the patient’s code status, and the pattern of hospital admissions in the 30 days before the current one. Patients scoring above a threshold are routed into a transitions program with more intensive follow-up.
This approach illustrates a broader principle: individual data sets become more powerful when linked. OASIS functional scores combined with RPM vital signs, claims history, and medication data create a far more complete picture of a patient’s risk than any single source. Agencies that invest in interoperable electronic health records, allowing these data streams to feed into a unified dashboard, can shift from reactive care to proactive intervention. The data already exists in most home health organizations. The difference between average and excellent outcomes often comes down to whether anyone is looking at it in time to act.