Tools for Measuring Quality in Healthcare

Tools for Measuring Quality in Healthcare

Quality measures are used to assess the overall quality of a hospital. Quality measures include patient safety indicators, staffing levels, and other clinical outcomes. The measure used in hospitals is Patient Safety Indicators. Patient safety indicators are collected using the “Joint Commission’s National Patient Safety Goal for Hospital Quality,” designed to measure patient safety. The Joint Commission uses hospital data to calculate its national patient safety goal. It determines the total number of patients at each hospital who had an incident involving harm to either themselves or another person in the previous year (or the two years prior if there were no incidents) (Bull et al., 2019). Three important rate-based quality measures are discussed in this paper: Readmission Rates, Adverse Drug Events per 1,000 doses, and Acute Care Inpatient and Observation Patient throughput-rated.

Rate-Based Measurements

There are many ways to gauge quality, and one of them is rate-based measurement. The rate-based size of quality is a method that involves measuring the health outcomes of a population over time. The process starts by measuring and quantifying the health status of a group of people, who are then compared to another group of people. For example, let us say you want to measure how many children in your community are obese. You would first collect data on the number of children who are obese, then compare it to the number who are not obese. If your data shows that more than half of your children are obese, then you have high rates of obesity among children in your community (Dewa et al., 2017). Using rate-based measurements helps healthcare providers and organizations identify areas where they can improve services or programs; this can help them develop strategies for improvement and guide future decisions about funding priorities and program development.

Acute Care Inpatient and Observation Patient Throughput

Definition

Discharge of patients from an acute care hospital during the same fiscal year after being admitted via the emergency room (Institute for Health Improvement).

Numeric Description

The measure measures the ratio of acute care inpatient discharges to acute care observation discharges for a given hospital. The numerator is the number of inpatients discharged from a critical care inpatient unit, and the denominator is the number of observation patients who meet the criteria for observation status.

Data Collection

The data for these measures are collected by trained staff observing and reviewing medical records. These staff members then rate the hospital based on their findings. This measurement represents the quantity of patients who were released from an emergency department hospitalization after 24 hours.  This is accomplished using an automated system that extracts information about discharge from telemedicine (EMR) (Bull et al., 2019). The EMR system allows hospitals to track all patients in real-time to monitor whether or not they are meeting this standard. CMS collects this data from each hospital by reviewing their medical records, including date and time. This measure is reported separately for general medical and surgical hospitals.

Measurement Comparison

This measurement is compared externally to other settings and is expressed as a percentile ranking. For example, if a hospital has a throughput of 80%, it means that 80% of its beds are filled with patients. If another hospital has a throughput of 75%, then it means that 75% of its beds are filled with patients.

Risk

The throughput measure for acute care inpatients and monitoring patients is a risk-adjusted by diagnosis-related group (DRG). A DRG is a set of diagnoses that have similar resource requirements. The risk adjustment model estimates the probability that an individual will incur high costs based on their diagnosis and procedure codes (Dewa et al., 2017). The denominator for this measure includes all patients in the same DRG with a discharge date within 30 days of the observation date (or admission date if it was an observation stay). This ensures that the denominator includes only patients at risk of high costs based on their diagnosis codes.

Setting of Goals

In this case, the health facility should identify the number of patients that need to be discharged daily and the average length of stay (LOS). The hospital should then calculate how many beds they have available and how many they need based on the above calculation. Once this is done, they will know how many patients are discharged daily. They can then set a goal for how many patients they will discharge daily. The number of patients admitted per hour is calculated by "dividing the total number" of admissions by the overall time since the day's starting in order to set this goal (or week). For example, if your hospital has 100 entries over 12 hours, and each admission takes about 15 minutes, then the average daily throughput for your hospital is 100 / (12 x 60) = 6.67 entries per hour.

Importance of the Measure

The quantity of patients seen by a doctor or a healthcare facility is measured by the acute care inpatient and assessment patient throughput. This is important to measure because it indicates how many patients are coming through the doors of a clinic or hospital. This metric can be used for both acute care hospitals and outpatient facilities. The measure evaluates whether the organization’s operations can accommodate the current number of admitted patients. An increase in throughput indicates that an organization can accommodate additional patients without experiencing significant delays in their care, suggesting that it can meet patient demand. Inpatient volume directly affects staffing levels and increases costs. The higher the volume, the more staff is needed to maintain quality care and safety. Observation of patient throughput indicates how much a hospital provides non-acute (non-emergency) care. This metric is vital because observation patients are not admitted to hospital beds but stay in a designated area within a hospital’s emergency department. Observation patients are typically uninsured or covered by Medicare, so they may not be paying the total price for their services (Dewa et al., 2017). For this reason, hospitals often have lower reimbursement rates for these patients than for inpatients or outpatients who pay their way (commercial insurance coverage).

Relations to Safety, Quality, and the Cost of Healthcare Delivery

Because it enables hospitals to assess whether their processes and procedures are operating as intended, patient throughput can be used as a sign of patient safety. Increased patient admissions are a sign of subpar patient care provided by the hospital.  High throughput rates shows that healthcare facilities are accepting more patients than they should, which may result in mistakes being made during treatment or discharge (Kilbourne et al., 2018). This can have severe consequences for patient safety and costs associated with poor quality care delivery. It relates to patient safety because it was chosen based on its ability to predict the risk for pressure ulcers, which are associated with increased mortality and cost. Pressure ulcers are often caused by not having enough staff, not enough medical supplies or equipment, and a lack of attention to detail. This results in a higher risk for patients developing pressure injuries while in the hospital.

Adverse Drug Events per 1,000 Doses

Definition

Patients receiving medications during the reporting period had their rate of adverse drug events (ADEs) reported (Institute for Health Improvement). Allergic reactions, anaphylaxis, and other serious ADEs are included in this measure. Adverse drug events per 1,000 doses include preventable and non-preventable ADEs (Kilbourne et al., 2018). Non-preventable adverse drug events include those caused by factors such as patient errors or underlying disease processes that are not under the control of medical staff. Preventable adverse drug events include those caused by medication errors, including wrong dosage or administration route; lack of physician orders; wrong patient; incompatible medications; mislabeled medication; and wrong dose strength (Kupeli et al., (2019).

Numerical Description

The numerator is the number of adverse drug events during each period, while the denominator is the number of doses administered during each period.

Data Collection

Pharmacies submit reports electronically through their electronic medical record systems using a standard form with fields that have been designed for this purpose; these fields include information such as patient identification, medication identification, and dosage amount (Kupel et al., 2019).

Measurement Comparison

This measurement is expressed as a rate (per 1,000 doses) and not as a percentile ranking. A rate can be considered an average or mean value, while a percentile ranking is the percentage above or below the average or mean value (Ma et al., (2020). For example, if there were one adverse drug event for every 100 doses received by patients in one unit during one year, this would be expressed as a rate = 0.01 (per 1,000 doses).

Risk

Age, gender, comorbidities, and drug class are risk adjustments that are assessed using a multivariate logistic regression model (Sprockett, 017). This model predicts whether or not a drug will cause an adverse event based on these patient characteristics and then calculates the number of adverse drug events per 1,000 doses dispensed for each drug by multiplying the predicted probability by the number of administered doses for each drug within each provider type (hospital outpatient setting) and specialty (Sprockett, 017).

Setting of Goals

Reducing the number of adverse drug events for every 1,000 administered doses is the aim of this statistic. This can be accomplished by ensuring that medications are administered correctly and that proper education is provided to all staff members administering medication. The organization has set a goal for this measure: to decrease adverse drug events by 30 percent over three years (Vaidya and Boes, 2018). To achieve this goal, the department will need to add additional staff members who have experience in administering medications without causing harm to patients

Importance of the Measure

This measure measures how many adverse drug events occur in one thousand doses. This number must be low for any given organization because it shows patients’ safety when receiving medications from their doctors or nurses. If this number is high, it is possible that some of these adverse drug events may be preventable and could be avoided by changing methods and improving processes at the facility (Vedam et al., 2017). If this number is low, it shows that there are few problems with medication intake at this facility, and no improvement needs to be made to keep patients safe while receiving treatment.

Relations to Safety, Quality, and the Cost of Healthcare Delivery

ADEs can impair the quality of life and contribute to death. They may also increase health care costs. The cost of preventing an adverse drug event is generally less than treating a patient who has experienced an ADE. This estimator is used as a hold for patient safety because it tracks the number of people harmed by subpar medical care, which can result in patients and family members deaths or disabilities (Dewa et al., 2017). It makes cost estimates for both direct costs, such as lost wages and medical expenses, and indirect costs, such as increased risk of illness or a shorter life span (Dewa et al., 2017). This measure also reflects the overall cost of healthcare delivery because it captures data about how much money is spent on healthcare for those harmed by unsafe practices rather than on improving quality through education, training, and staffing changes.

Readmission Rate

Definition

the proportion of hospitalized patients who are readmitted within seven days of discharge.

Numerical Description

The proportion of readmissions for any reason other than death within seven days of discharge makes up the numerator. The denominator is the total number of discharges during that period.

Data Collection

The data are collected by reviewing medical records for each readmission event identified during a specified period. Data is also obtained from reviewing reports submitted by facilities participating in the program.

Measurement Comparison

This measurement is expressed as a rate (per 7 patient-days) and not as a percentile ranking. A rate can be thought of as an average or mean value.

Risk

The readmission rate does not require risk adjustment. Patients who are hospitalized within seven days are included in the hospitalization rates after discharge. While this may seem like a logical way to measure quality, it does not account for differences in types or severity of illness or complications that could lead to readmission (Fitzpatrick and Tumlinson, 2017)

Setting of Goals

The goal for the readmission rate is to decrease readmissions within seven days after discharge from hospitalization by 15 percent over five years. To achieve this goal, the department must educate patients on preventing readmissions by following their physician’s orders regarding medications and lifestyle changes after discharge from hospitalization (Vedam et al., 2017). Moreover, this can also be accomplished by providing more aftercare options for discharged patients and by making sure that follow-up appointments are scheduled for all discharged patients at least two weeks after their discharge date.

Importance of the Measure

The measure is used to track the effectiveness of discharge planning by identifying cases where services were not provided or fully provided before discharge from the hospital. A high readmission rate may indicate gaps in continuity of care between acute care hospitals and other providers, such as home health agencies or nursing homes (Hanefeld et al., (2017). If a person returns within seven days of being discharged from hospitalization, they are readmitted and will need additional care after completing their initial treatment. This measure is essential because it shows how well hospitals treat patients after discharge so as not to unnecessarily prolong recovery time or cause complications due to mistakes made during that period.

Relations to Safety, Quality, and the Cost of Healthcare Delivery

This is utilized as patient safety indicator. The higher the readmission rate, the greater the risk that patients will not receive appropriate care from their physicians and medical staff, which can result in complications that could lead to an increased length of stay or even death. High readmission rates may also reflect poor coordination among medical specialties, such as primary care physicians and surgeons. High readmission rates are often caused by medical errors and infections contracted from medical equipment used during surgery or treatment (Bull et al., 2019). They can also result from failure to follow up with discharged patients by doctors and nurses. If a hospital has a high readmission rate, there are gaps in care or patient education, leading to worse outcomes and higher costs for the provider and patient. It can also determine how healthy hospitals are following best practices for treating patients with certain conditions. For example, a hospital with a high readmission rate for pneumonia may not follow best practices or provide adequate follow-up care after discharge. This could be due to insufficient staffing levels or inadequate staff training who provide home care services (Cooper et al., 2017). High readmission rates impact the overall cost of healthcare delivery because they increase costs related to readmitting patients and extending their hospital stays. 

References

Bull, C., Byrnes, J., Hettiarachchi, R., & Downes, M. (2019). A systematic review of the validity and reliability of patient‐reported experience measures. Health services research, 54(5), 1023-1035.

Cooper, V., Clatworthy, J., Harding, R., & Whetham, J. (2017). Measuring quality of life among people living with HIV: a systematic review of reviews. Health and quality of life outcomes, 15(1), 1-20.

Dewa, C. S., Loong, D., Bonato, S., & Trojanowski, L. (2017). A systematic review is a relationship between physician burnout and healthcare quality regarding safety and acceptability. BMJ Open, 7(6), e015141.

Fitzpatrick, A., & Tumlinson, K. (2017). Strategies for optimal implementation of simulated clients for measuring the quality of care in low-and middle-income countries. Global Health: Science and Practice, 5(1), 108-114.

Hanefeld, J., Powell-Jackson, T., & Balabanova, D. (2017). Understanding and measuring the quality of care: dealing with complexity. Bulletin of the World Health Organization, 95(5), 368.

Institute for Health Improvement (n.d). Quality Improvement Essentials Toolkit - Ihi. (n.d.). Retrieved June 18, 2022, from http://www.ihi.org/resources/Pages/Tools/Quality-Improvement-Essentials-Toolkit.aspx

Kilbourne, A. M., Beck, K., Spaeth‐Rublee, B., Ramanuj, P., O’Brien, R. W., Tomoyasu, N., & Pincus, H. A. (2018). Measuring and improving the quality of mental health care: a global perspective. World Psychiatry, 17(1), 30-38.

Kupeli, N., Candy, B., Tamura-Rose, G., Schofield, G., Webber, N., Hicks, S. E., ... & Aspden, T. (2019). Tools measuring the quality of death, dying, and care, completed after death: a systematic review of psychometric properties. The Patient-Patient-Centered Outcomes Research, 12(2), 183-197.

Ma, L. L., Wang, Y. Y., Yang, Z. H., Huang, D., Weng, H., & Zeng, X. T. (2020). Methodological quality (risk of bias) assessment tools for primary and secondary medical studies: what are they and which is better?. Military Medical Research, 7(1), 1-11.

Sprockett, A. (2017). Review of quality assessment tools for family planning programs in low-and middle-income countries. Health policy and planning, 32(2), 292-302.

Vaidya, S., & Boes, S. (2018). Measuring quality of life in children with spinal muscular atrophy: a systematic literature review. Quality of Life Research, 27(12), 3087-3094.

Vedam, S., Stoll, K., Rubashkin, N., Martin, K., Miller-Vedam, Z., Hayes-Klein, H., & Jolicoeur, G. (2017). The Mother on respect (MOR) index: measuring quality, safety, and human rights in childbirth. SSM-population health, 3, 201-210.