Traditional software companies built applications to solve one part of the patient-data life cycle at a time: scheduling, coding, billing, accounts-receivable clearing, and so on. Practices are left on the hook to configure each application, link them together, and keep data flowing consistently between them. They also have to create security rules that comply with HIPAA while still giving people the access they need for data troubleshooting and support.
Data that works for you
Dog Star AI envisions a new era of healthcare services software, where data works for practice groups and administrators instead of creating more work for them. Governed data is gathered and monitored up front to prevent quality issues at critical process steps. Modules are architected to integrate from the start. Functionality is tailored to the healthcare services industry, so there’s no need for complex configuration or teams of people to translate and transform data at each step of the process.
That’s why we treat gathering, organizing, and monitoring data as foundational work. It includes publicly available data, such as NPPES records with the NPI and TIN numbers of facilities and clinicians. It also includes more complex data, such as the negotiated payer rates published under CMS price transparency rules. The critical idea is getting data to work for you before you click a single mouse. Higher data quality, fewer manual lookups, and quality-control checks at the earliest steps of the process turn data you work for into data that works for you on day one.
Built on a governed foundation
On this foundation, we are building a suite of software applications that prioritize data integration, quality, and transparency across the core of the healthcare services industry. Our Shift-IQ product shows how we apply advanced technology to fundamental challenges facing today’s practices. Clinicians need fair, transparent scheduling, without feeling punished by inflexible algorithms that, for example, assign a night call shift after every vacation. More importantly, they need confidence that scheduling decisions are made with integrity, with the best outcomes for both patients and clinical teams in mind, and that those outcomes aren’t sacrificed to cost-management pressure. At the same time, administrators need visibility into the resources required to meet their obligations, the makeup of their staff, cash-flow forecasts, and the cost drivers they must balance against excellence in service delivery.
Responsible AI, by design
Dog Star’s commitment to responsible AI means we never generate a practice schedule with a large language model or other generative-AI “black box.” Instead, we use AI to expand the creativity of our technical solutions and the service and value they deliver. Shift-IQ is built on a proven optimization engine maintained by Google, the same kind of technology used across industries for complex assignments, from operating rooms to flight crews. It works from constraints tailored to your practice and the fairness principles you define for your clinicians. It first minimizes clinician dissatisfaction within those constraints to find qualifying schedules. Only then does it look for efficiencies, such as the balance between employed clinicians and locum tenens coverage.
Reimagining software services
Our vision embraces the full, transformative potential of AI to create data assets and software services tailored to healthcare services. Many companies, in healthcare and beyond, are confronting data silos and technical debt as they try to put AI to work. Dog Star AI takes a different route: we’re reimagining what software services can be. The principle guiding us is simple. Software can only succeed if it relentlessly addresses its customers’ needs, including all of their core data needs, from security and governance to interoperability and decision support.
Let’s talk
Would you like to see a Shift-IQ proof of concept using your practice’s data, or become a beta tester for our CMS Negotiated Rates Browser data product? Reach out and let us know. And tell us: how are you working for your data today, and in your ideal world, how should it work for you?