AHRQ Health IT Update: Medication Regimen Complexity

Agency for Healthcare Research and Quality (AHRQ) sent this bulletin at 02/11/2013 07:20 AM EST

Automation of the Medication Regimen Complexity Index

According to a study supported by the Agency for Healthcare Research and Quality, difficulties in taking medicines are impacted by more than just the number of pills patients take. How often a medication is taken, whether the dose is always the same, and whether the medicine it is administered by mouth, through a shot, or through a patch, all have an impact on how hard it is for someone to follow their doctors’ or nurses’ instructions. Researchers had previously developed a medication regimen complexity index (MRCI) to help health care teams calculate the difficulty of their patients’ medicine schedules. A recent study suggests that researchers may be able to use computers to automatically tabulate the difficulty of following health care providers’ medication instructions. Automated calculations would allow health care teams to identify patients who are at higher risk of adverse events and could potentially be used to improve medication management and patient outcomes. The study, “Automating the Medication Regimen Complexity Index,” appeared in the Journal of the American Medical Informatics Association. To access the abstract, select: www.ncbi.nlm.nih.gov/pubmed/23268486.

2012 Dec 25. [Epub ahead of print]

Automating the medication regimen complexity index.

McDonald MV, Peng TR, Sridharan S, Foust JB, Kogan P, Pezzin LE, Feldman PH.

Source

Center for Home Care Policy and Research, Visiting Nurse Service of New York, New York, New York, USA.

Abstract

OBJECTIVE:

To adapt and automate the medication regimen complexity index (MRCI) within the structure of a commercial medication database in the post-acute home care setting.

MATERIALS AND METHODS:

In phase 1, medication data from 89 645 electronic health records were abstracted to line up with the components of the MRCI: dosage form, dosing frequency, and additional administrative directions. A committee reviewed output to assign index weights and determine necessary adaptations. In phase 2 we examined the face validity of the modified MRCI through analysis of automatic tabulations and descriptive statistics.

RESULTS:

The mean number of medications per patient record was 7.6 (SD 3.8); mean MRCI score was 16.1 (SD 9.0). The number of medications and MRCI were highly associated, but there was a wide range of MRCI scores for each number of medications. Most patients (55%) were taking only oral medications in tablet/capsule form, although 16% had regimens with three or more medications with different routes/forms. The biggest contributor to the MRCI score was dosing frequency (mean 11.9). Over 36% of patients needed to remember two or more special instructions (eg, take on alternate days, dissolve).

DISCUSSION:

Medication complexity can be tabulated through an automated process with some adaptation for local organizational systems. The MRCI provides a more nuanced way of measuring and assessing complexity than a simple medication count.

CONCLUSIONS:

An automated MRCI may help to identify patients who are at higher risk of adverse events, and could potentially be used in research and clinical decision support to improve medication management and patient outcomes.