Hospital Intelligence Brief

What SPARCS Data Can Reveal About New York Hospital Markets

New York’s public discharge data shows where patients are treated, hospital by hospital and service by service. It does not show what the care paid or cost, which limits what it can prove.

1. The business problem

What can public discharge data tell you about your hospital’s market, and where does it stop?

Every hospital wants to know how it is doing against its neighbors: who is gaining patients, in which services, and where residents are going instead. Public discharge data is one of the few sources that answers those questions for everyone in a market at once.

This matters because market decisions (investing in a service, recruiting a specialty, responding to a competitor) depend on knowing demand and share. But discharge data answers “where did patients go?” not “was it worth it?” Mixing those up is the most common mistake.

2. What the evidence can reveal

Verified fact The Statewide Planning and Research Cooperative System (SPARCS) is New York State’s data system for patient-level information on hospital discharges, ambulatory surgery and emergency department visits. De-identified public-use files are published by the state. Where charges appear in such data, they are not the same as what was paid or what care cost.

Within those limits, discharge data can support several useful comparisons:

  • Calculated Volume and share: discharges by hospital and service category across a defined market.
  • Calculated Trends: which hospitals and services are gaining or losing over time.
  • Calculated Patient origin (where the data supports it): which areas supply a hospital’s patients, and which residents go elsewhere.
  • Information gap Not included: reimbursement, cost, contribution margin, the reason a patient chose a hospital, or causation.

The synthetic market in this article is illustrative and is not New York data. It is built to behave like the public discharge data described above, so you can practice reading it safely.

3. Investigate the problem

In the synthetic market, our hospital’s discharges moved from 6,043 to 5,888 (−2.6%) while the market moved from 21,800 to 22,300 (+2.3%). Our share moved from 27.7% to 26.4%. Illustrative

Illustrative: discharges and share in a synthetic five-hospital market, FY25 to FY26
HospitalDischarges FY25Discharges FY26Share FY25Share FY26
Demo Community Hospital (us)6,0435,88827.7%26.4%
St. Mary’s Regional5,1135,81523.5%26.1%
Riverside Health4,2754,09519.6%18.4%
County General3,6303,70216.7%16.6%
Valley Medical2,7392,80012.6%12.6%

Calculated The market grew modestly and we shrank, so our share slipped, while St. Mary’s Regional gained 2.6 pts of share. Within our own discharges, Obstetrics fell 13.5%, Orthopedics 3.2% and Oncology 6.3%, while General Surgery rose 5.4%.

Hypothesis Share loss in specific services could reflect a competitor’s new physicians or program, a change in access, or a referral shift. Discharge data can show that it happened and where. It cannot show why, or whether the lost cases were ones we would have wanted.

4. Explore the interactive experience

Investigate it yourself.

Hospital Market Explorer: a market dashboard comparing a five-hospital market by service category, an outmigration capture scenario, evidence-linked answers from APIS, and a sample brief, all on synthetic data.

The experience below uses a synthetic five-hospital market built to look like public discharge data. It is not New York data, or data for any real market, and every figure is illustrative. The counts are discharges, not dollars.

  1. Select the Orthopedics row in the service-category table. The hospital table below re-filters to that category. Who gained?
  2. Compare overall share with share in one category. Where is the story different?
  3. Ask APIS “What can this data not tell us about profitability?”

5. Evaluate possible solutions

Discharge data is a starting point for questions. These options turn what it shows into next steps.

Investigate the categories losing share

When it may fit: A category lost share while the market was stable or growing.

Evidence needed: Patient origin, referral patterns, physician changes by category.

Trade-off: Share loss may reflect a deliberate choice or factors outside the hospital’s control.

Defend what is working

When it may fit: A category is gaining or holding share.

Evidence needed: The drivers of success, such as physicians and access.

Trade-off: Assuming it will continue without watching it can be costly.

Add financial data before any investment

When it may fit: A share gap suggests an opportunity.

Evidence needed: Payer mix and contribution per case in that category.

Trade-off: Skipping this risks chasing volume that does not pay.

Monitor competitors

When it may fit: A competitor is gaining quickly.

Evidence needed: Competitor service launches and recruiting.

Trade-off: Reacting to share alone can lead to expensive responses.

6. What leadership should do next

Questions to answer

  • Where is our share rising or falling, by service category?
  • Who is gaining, and in what?
  • Is the market itself growing or shrinking?
  • What can this data not tell us?

Data to gather

  • Discharge data by hospital and category, multiple years
  • Patient origin by ZIP code or county
  • Payer mix and contribution by category
  • Physician and service changes at competitors

Steps to take

  1. Define the market area and service categories.
  2. Build share and trend by category.
  3. List the categories worth a closer look.
  4. Add financial and operational evidence before deciding.

7. Engage Two Pillars

Explore Your Hospital’s Opportunities

Two Pillars can help hospital leadership investigate market, financial, operational, and strategic questions using relevant organizational data, public information, and a structured assessment process. A good first step is a market intelligence review that turns public discharge data into a short list of questions worth answering with your own data.

Sources and data notes

  • The organization, its figures, and every number in this article and its interactive experience are synthetic and illustrative. They are not real data, a benchmark, or a forecast.
  • New York State Department of Health, SPARCS: www.health.ny.gov/statistics/sparcs/ — information on the Statewide Planning and Research Cooperative System and its public-use data files.
  • CMS, Cost Reports: www.cms.gov/data-research/statistics-trends-and-reports/cost-reports — public information on the annual cost reports that Medicare-participating hospitals file.
  • Reviewed October 2026. This article describes an approach to investigation. It does not replace review by qualified professionals.