Quality of Care White Paper Series

10 "Must-Haves" in a Clinical Quality Management System

Must-Have #1
An All-Patient, All-Problem Registry of Clinically-Verified Information

In most cases, the data used to document a patient's problems is administrative data, the data used for billing. As is widely known, this data was never meant to be a source of clinical informaton on a patient; it was meant to provide data for reimbursement. As such, when it is used to manage patient care, can lead to incorrect conclusions, improperly affect decisions, frustrate providers and penalize a practice when participating in a pay-for-performance program.

A registry is a feature of a clinical quality management system that captures and manages information on a practice's patients. An effective registry in a clincial quality management system must track all of a practice's patients, all patient problems and in a manner unbiased by administrative data.

All-Patient, All-Problem Registry
To provide comprehensive and thorough care, clinicians and practices need a complete and accurate understanding of all their patient's problems. Limited information in this area can result in incorrect care, unnecessary treatments and waste provider time.

A registry needs to track all patients and all patient problems—acute, chronic and lifestyle. Having a complete picture allows a provider to fully understand the needs of their patient popultion and manage patients based on multi-morbitity issues. For instance, patients with both diabetes and depression could have different treatment recommendations than those with just diabetes.

By accurately capturing and tracking a patient's entire set of problems, a practice not only supports the current quality improvement needs of a practice, it provides the support for future and yet unknown programs. As new pay-for-performance programs proliferate, their care scope will continue to broaden. Without an all-problem registry, each new increase in scope will require the development of a separate registry, saddling a practice with increased costs and preventing a practice from participating in that program immediately. The development and population of a registry can take up to a year, at significant time and cost.

Managing patient populations without an all-patient registry can be misleading. Missing patient data can compromise the denominator of a population measure, and because such data can be difficult to identify, the error is nearly impossible to correct. Incorrect patient populations can negatively impact quality calculations with negative financial implications.

From a workflow perspective, if a registry only manages a subset of a practice's patients, it is difficult for the practice to incorporate its use into routine activities, and any tool that is not part of a practice's routine will suffer in adoption. This prevents the value of a registry from being realized by a practice.

And, finally, an all-problem registry also allows a provider to track patient episodes of care. Understanding how specific problems evolve and change over time can be an important knowledge source for care management. This supports patient-centered home and chronic care models.

A registry must track the entire patient population of a practice and the entire set of problems associated with each patient.

Clinicially-Verified Problem Lists
When documenting problems, the critical aspect of a registry is the use of clinical and clinicially-verified data.

Diagnoses and problems captured from an adminstrative (or billing) data set can be inaccurate in a variety of ways. Billing systems often "optimize" diagnoses codes for reimbursement and may suggest changes in an original diagnosis title. In addition, they only include diagnoses required for reimbursement and not the entire set of patient problems. This phenomenon was documented in a recently published report entitled "Health Information Technology for Improving Quality of Care in Primary Care Settings" by The Agency for Healthcare Research and Quality and it underscores the issues of using billing or claims data to understand clinical issues:

"... in many health IT systems, patients with asthma do not have a diagnosis of asthma; they have a data history of billed visits with a billing diagnosis code of asthma. For visits to the clinic that did not involve their asthma (and hence no billing code of asthma was issued), there is no way to relate that visit to their chronic condition of asthma. Additionally, an asthma billing code is often used for a patient who arrives wheezing (whether they have a diagnosis of asthma or not)."¹

"This may not look like a data problem on the surface, but if you ask the health IT system how many asthmatics are in a panel, the numbers may be far from reality."¹

Solving this issue is quite simple. Regardless of the source of diagnosis information, it must be clinican-verified, complete and unbiased by administrative systems. Clinicians should document or verify any diagnosis. The list of problems should not be abridged or limited. And, finally, the problem set should not change based upon the activities of a billing system. Verification of diagnoses by a clincian at the point-of-care is quite simple and adds little time to an encounter.

In closing, there is one other issue to consider. Any management system or information system, to be accepted and utilized, must be trusted. If a provider is given a system that they don't trust, they won't use it. And that is a complete waste of everyone's precious resources—time and money.

Donald E. Nease, Jr., MD is Associate Professor and Family Physician Researcher in the Department of Family Medicine at the University of Michigan Health System and Chief Medical Officer at Cielo MedSolutions. David J. Morin is Chief Executive Officer at Cielo MedSolutions.

Get an all-patient, all-problem registry with Cielo Clinic.