Table 3.
Strengths and weaknesses assessment of each composite quality measure
| Instrument | Strengths | Weaknesses | |
|---|---|---|---|
| Minimal clinical input | DAOH21 | Simple to calculate. Uses data points that are routinely collected and available. Similar approach to HARM and MTL | Simplistic approach. Includes variables on mortality rate and length of stay but ignores other quality indicators |
| HARM3,23–25 | Simple to calculate. Uses data points that are routinely collected and available. Uses more data points than DAOH. Similar approach to MTL and DAOH | Simplistic approach. Includes variables on length of stay, readmission and mortality rate but ignores other quality indicators | |
| MTL27 | Simple to calculate and uses data points that are routinely collected and available. Uses more data points than DAOH. Similar approach to HARM and DAOH | Simplistic approach. Includes variables on mortality rate, transfer to another hospital and length of stay but ignores other quality indicators | |
| Moderate clinical input | TO28 | Can be tailored to suit any procedure. Quality indicators chosen by expert opinion | Simplistic approach which assumes all selected short-term outcomes have equal importance. Subjective. May require more data than routinely collected and available |
| PQS31 | Simple to calculate. Data points are routinely collected and available. Assesses more quality indicators (10) than most other scores. Quality indicators chosen by Delphi consensus survey | May require more data than routinely collected and available even though it is designed to be used with existing records | |
| DIMICK32 | Uses data points that are routinely collected and available. Utilizes quality information from other related procedures to improve precision of quality measurement for each operation. Weights are calculated for each quality indicator to improve precision | Dependence on a database collected by others. Less simple to calculate, requires statistical support | |
| PMI29 | Data points are routinely collected and available. Incorporates already validated grading systems | Dependence on a database collected by others. Less simple to calculate, may require statistical support. | |
| Significant clinical input | SCOUT26 | Detailed analysis using many different data points. Quality indicators chosen by expert opinion | Requires manual collection of the outcome metrics used |
| I-FEED22 | Detailed analysis using many different data points | May be expensive and time-consuming to run. Ileus only one relevant outcome. Requires expertise | |
| TISS30 | Detailed analysis using many different data points. Has been widely used | Requires ICU-level equipment which can automatically collect vast amounts of data. Requires expertise |
LOS, length of (hospital) stay; HARM, Hospital stay, Readmission, and Mortality; DAOH, Days Alive and Out of Hospital; SCOUT, Surgical Complication OUTcome; MTL, Mortality, Transfer, Length-of-stay; TO, Textbook Outcome; NSQIP, National Surgical Quality Improvement Program; TISS, Therapeutic Intervention Scoring System; I-FEED, Intake, response to nausea treatment, Emesis, Exam, and Duration; PQS, Patient Quality Score; PMI, Post-operative Morbidity Index; DIMICK, Dimick et al. 2013.