Quality Data Collection Grading Criteria, Business Proposal Example

Introduction

The Blue Canyon Pain Management Center is a clinical center that focuses on reducing pain associated with acute and chronic conditions.  In addition to patients with the latest pharmacological solutions, the center also offers alternative pain management solutions including psychological therapy.  Although the Center has been open for more than three years, there are two main areas to improve on: 1) Pain medication errors; 2) patient satisfaction with pain solutions.

Data Collection Tools

In order to understand the current level of pain medication errors,  two data collection will be studied.  The first is a computerized analysis to identify patients that receive drugs to treat a medication error or searching drug concentrations that may indicate a drug overdose (Bates, D.W., Cullen, D.J., Laird, N et al, 1995).

One of the main problems in assessing and understanding medication errors is data collection.  In some instances, medication errors arrive from poor handwriting or the miscommunication of a drug or dosage from one health professional to another; other times, the pharmacist simply fills the wrong prescription or hands the wrong drugs to the patient. The first data collection tool establishes an aggregate list of medications prescribed to counteract the effects of a medication overdose or a search of drugs and dosages prescribed that may be the wrong drug for a condition or over prescribed.

The strength of the toll is the ability to give an aggregate estimate of medication errors in a particular facility.  The weakness of the tool is its ability to capture certain errors.  For example, medication will only be prescribed to counteract an overdose in the most serious of cases- many non-severe cases will be missed by this method.  In addition, the computer’s database might not be complete or may also contain errors: In this case, a true picture of the existing level of medication errors will not be possible.

The second data collection tool aims at a more specific goal: the potential disconnect between the drugs doctors prescribe and pharmacists dispense.  This data collection tool takes data from the prescriptions sent by doctors and double checks them against the actual drugs pharmacists give out. (Shuttleworth & Ruelle, 1996).  The strength of this data collection tool is the ability to focus on one piece of the medication error puzzle; that is, it allows one to understand to what extent the disconnect between doctors and pharmacists is causing medication errors.

The third data collection tool focuses on the issue of patient satisfaction.  Often times, clinics measure patient satisfaction in a number of ways such as return visits or complaints lodged with managers or the insurance company.  Clinics very rarely, however, engage in a systematic survey of their patients to understand whether they are satisfied with pain management therapies.  There are a number of different survey instruments that could be used to gauge customer satisfaction including  the brief pain survey (BPS) and the McGill Short-Form Pain Questionnaire (Gordon, Pellino, McNeill, Paice, Laferriere & Bookbinder, 2002).

Quality Improvement Tools

The main strength of these survey instruments is obtaining data directly from the patient.  Often times, clinicians attempt to quantify pain from a number of different proxies.  A pain survey, used in an appropriate context, can give direct information to the clinician.  The main drawback of using surveys is issues related to self-reported data; some self-reported data is  not valid due to the individual’s (mis) understanding of questions or other issues related to survey data.  Overall, the data collection tools examined are a good first step in understanding the size and the scope of the problem.  The main problem with all three tools is they are retrospective data and cannot be examined in real time since these errors must be corrected immediately, these tools can only serve as guidance for future quality improvement efforts.

Electronic health records (EHR) is a  quality improvement process used to prevent medical errors.  EHR works at a number of margins to prevent medication errors.  First, since the prescriptions are delivered via IT systems to the pharmacy, poor handwriting is not a concern.  Second, and perhaps more important, EHR cuts down on the incorrect prescriptions of certain drugs by flagging prescriptions for doctor and pharmacist approval.  This correction process happens in real time allowing medical personnel to change the prescription if necessary.  Third, since the EHR system contains a patient’s entire medical history, it is easy to assess previous medication errors, as well as prevent future ones.  EHR can also be used to display the data gathered from the two data collection tools above.

The tool’s main strength is the compendium of information that it offers in addition to the ability to change prescriptions.  The downside of EHR is the system is only as “smart” as the people that use it.  Some medication errors based on poor systems will inevitably continue, especially as they adapt to learning new technology.

The inclusion of patient survey data in the patient’s file for doctor and pharmacist is the second QI tool that is proposed.  Often times, doctors will ask patients on an appointment by appointment basis their pain status on that day versus previous days.  While the question should be asked, often times, neither the patient nor the doctor understands the trajectory of pain.  Inclusion of the patient pain surveys into medical records would allow clinicians to understand how the patient has described pain in the past and also ascertain potential solutions for the future.

References

Bates, D.W., Cullen, D.J., Laird, N et al. (1995) Incidence of adverse drug events and potential adverse drug events. JAMA. 274 (1), 29-34.

Gordon, D.B., Pellino, T.A., McNeill, J.A., Paice, J.A., Laferriere, D & Bookbinder, M.  A. (2002). 10-year review of quality improvement monitoring in pain management: recommendations for standardized outcome measures.  Pain Management Nursing , 3(4), 116-130. 

Shuttleworth, T.A., Ruelle, S.  (1996) Detecting medication errors with automated distribution. Paper presented at ASHP Midyear Clinical Meeting. New Orleans, LA; 1996 Dec 11.