Practice Management Performance Indicators in Academic Radiology Departments

Development of Quality Indicators for the Ultrasound Department through a Modified Delphi Method

This study aims to establish precise quality indicators for evaluating and enhancing ultrasound performance, employing a methodology based on a comprehensive review of the literature, expert insights, and practical application experiences. We conducted a thorough review of both the domestic and international literature on ultrasound quality control to identify potential indicators. A dedicated team was formed to oversee the complete indicator development process. Utilizing a three-round modified Delphi method, we sought expert opinions through personalized email correspondence. Subsequently, data from diverse hospital indicators were collected to validate and assess feasibility. A novel set of seven indicators was compiled initially, followed by the convening of a 36-member nationally representative expert panel. After three rounds of meticulous revisions, consensus was reached on 13 indicators across three domains. These finalized indicators underwent application in various hospital settings, demonstrating their initial validity and feasibility. The development of thirteen ultrasound quality indicators represents a significant milestone in evaluating ultrasound performance. These indicators empower hospitals to monitor changes in quality effectively, fostering efficient quality management practices.

Productivity evaluation of radiologists interpreting computed tomography scans using statistical process control charts

Radiology service managers search for efficient ways to monitor productivity and improve capacity. One way to assess radiologists' productivity is by measuring their time to complete reports. Radiology reporting times (RRTs) may be monitored using statistical tools, such as process control charts (CCs). This study was carried out in the radiology sector of a University-based general hospital with 850 inward beds. Productivity was monitored using CCs. The selected control variable was RRTs, and process capability was calculated using C_p and C_pk indices. Only chest computed tomography scans were analyzed, totaling 2862 exams over a 6-month period. Our objective was to develop a simple tool to monitor radiologist performance, as given by RRT, over time. For that, we constructed CCs using data from 10 radiologists to monitor the stability of their RRTs. Only 3 radiologists presented mean times below the group average; 6 displayed a trend in RRTs that characterized performance improvement, while 4 displayed the opposite trend. Capability measures for the group indicated a process that is not capable. We demonstrate that CCs may be a useful tool for monitoring radiologists' performances in CT scans interpretation. Results demonstrated that in the individual CT reporting process, common cause variability is the type of variability most frequently observed, being most likely related to natural variations in features of the images analyzed. Lastly, CCs may also assist in decision making in the sector, such as establishing minimum productivity goals based on historical performance.

Artificial Intelligence for the Future Radiology Diagnostic Service

Radiology historically has been a leader of digital transformation in healthcare. The introduction of digital imaging systems, picture archiving and communication systems (PACS), and teleradiology transformed radiology services over the past 30 years. Radiology is again at the crossroad for the next generation of transformation, possibly evolving as a one-stop integrated diagnostic service. Artificial intelligence and machine learning promise to offer radiology new powerful new digital tools to facilitate the next transformation. The radiology community has been developing computer-aided diagnosis (CAD) tools based on machine learning (ML) over the past 20 years. Among various AI techniques, deep-learning convolutional neural networks (CNN) and its variants have been widely used in medical image pattern recognition. Since the 1990s, many CAD tools and products have been developed. However, clinical adoption has been slow due to a lack of substantial clinical advantages, difficulties integrating into existing workflow, and uncertain business models. This paper proposes three pathways for AI's role in radiology beyond current CNN based capabilities 1) improve the performance of CAD, 2) improve the productivity of radiology service by AI-assisted workflow, and 3) develop radiomics that integrate the data from radiology, pathology, and genomics to facilitate the emergence of a new integrated diagnostic service.

Smart chest X-ray worklist prioritization using artificial intelligence: a clinical workflow simulation

Objective

The aim is to evaluate whether smart worklist prioritization by artificial intelligence (AI) can optimize the radiology workflow and reduce report turnaround times (RTATs) for critical findings in chest radiographs (CXRs). Furthermore, we investigate a method to counteract the effect of false negative predictions by AI—resulting in an extremely and dangerously long RTAT, as CXRs are sorted to the end of the worklist.

Methods

We developed a simulation framework that models the current workflow at a university hospital by incorporating hospital-specific CXR generation rates and reporting rates and pathology distribution. Using this, we simulated the standard worklist processing “first-in, first-out” (FIFO) and compared it with a worklist prioritization based on urgency. Examination prioritization was performed by the AI, classifying eight different pathological findings ranked in descending order of urgency: pneumothorax, pleural effusion, infiltrate, congestion, atelectasis, cardiomegaly, mass, and foreign object. Furthermore, we introduced an upper limit for the maximum waiting time, after which the highest urgency is assigned to the examination.

Results

The average RTAT for all critical findings was significantly reduced in all prioritization simulations compared to the FIFO simulation (e.g., pneumothorax: 35.6 min vs. 80.1 min; p < 0.0001), while the maximum RTAT for most findings increased at the same time (e.g., pneumothorax: 1293 min vs 890 min; p < 0.0001). Our “upper limit” substantially reduced the maximum RTAT in all classes (e.g., pneumothorax: 979 min vs. 1293 min/1178 min; p < 0.0001).

Conclusion

Our simulations demonstrate that smart worklist prioritization by AI can reduce the average RTAT for critical findings in CXRs while maintaining a small maximum RTAT as FIFO.

Key Points

• Development of a realistic clinical workflow simulator based on empirical data from a hospital allowed precise assessment of smart worklist prioritization using artificial intelligence.

• Employing a smart worklist prioritization without a threshold for maximum waiting time runs the risk of false negative predictions of the artificial intelligence greatly increasing the report turnaround time.

• Use of a state-of-the-art convolution neural network can reduce the average report turnaround time almost to the upper limit of a perfect classification algorithm (e.g., pneumothorax: 35.6 min vs. 30.4 min).

A Business Analytics Software Tool for Monitoring and Predicting Radiology Throughput Performance

Business analytics (BA) is increasingly being utilised by radiology departments to analyse and present data. It encompasses statistical analysis, forecasting and predictive modelling and is used as an umbrella term for decision support and business intelligence systems. The primary aim of this study was to determine whether utilising BA technologies could contribute towards improved decision support and resource management within radiology departments. A set of information technology requirements were identified with key stakeholders, and a prototype BA software tool was designed, developed and implemented. A qualitative evaluation of the tool was carried out through a series of semi-structured interviews with key stakeholders. Feedback was collated, and emergent themes were identified. The results indicated that BA software applications can provide visibility of radiology performance data across all time horizons. The study demonstrated that the tool could potentially assist with improving operational efficiencies and management of radiology resources.

Quality metrics currently used in academic radiology departments: results of the QUALMET survey

OBJECTIVE

We present the results of the 2015 quality metrics (QUALMET) survey, which was designed to assess the commonalities and variability of selected quality and productivity metrics currently employed by a large sample of academic radiology departments representing all regions in the USA.

METHODS

The survey of key radiology metrics was distributed in March-April of 2015 via personal e-mail to 112 academic radiology departments.

RESULTS

There was a 34.8% institutional response rate. We found that most academic departments of radiology commonly utilize metrics of hand hygiene, report turn around time (RTAT), relative value unit (RVU) productivity, patient satisfaction and participation in peer review. RTAT targets were found to vary widely. The implementation of radiology peer review and the variety of ways in which peer review results are used within academic radiology departments, the use of clinical decision support tools and requirements for radiologist participation in Maintenance of Certification also varied. Policies for hand hygiene and critical results communication were very similar across all institutions reporting, and most departments utilized some form of missed case/difficult case conference as part of their quality and safety programme, as well as some form of periodic radiologist performance reviews.

CONCLUSION

Results of the QUALMET survey suggest many similarities in tracking and utilization of the selected quality and productivity metrics included in our survey. Use of quality indicators is not a fully standardized process among academic radiology departments. Advances in knowledge: This article examines the current quality and productivity metrics in academic radiology.

Factors associated with No-Shows and rescheduling MRI appointments

Background

One of the major challenges facing global radiology services comes from delays connected to long waiting lists for magnetic resonance imaging (MRI) procedures. Such delays in diagnostic procedures could lead to poorer patient care outcomes. This study intended to estimate the rate of “No-Shows” or “Reschedule” MRI appointments. We also investigated the factors correlating No-Shows and Reschedule MRI appointments.

Methods

A cross-sectional study was conducted in Saudi Arabia using data obtained via MRI schedule reviews and self-administrated questionnaires. Clinical and demographic data were also collected from the study participants. Stepwise binary logistic regression was used to analyze the data.

Results

A total of 904 outpatients were asked to participate in the study, and we enrolled 121 outpatients who agreed to complete the study questionnaire. Of the 904 outpatients, the rate of No-Shows or Reschedule was 34.8% (95% Confidence Interval: 31.7–38.1%). Of the 121 outpatients studied, the rate of No-Shows or Reschedule was 49.6% (95% CI: 40.4–58.8%). Those of the female gender (OR = 6.238; 95% CI: 2.674–14.551, p-value = 0.001) and lack of education (OR = 2.799; 95% CI: 1.121–6.986, p-value = 0.027) were highly associated with No-Shows for the MRI appointments. There was no clarification of the MRI instructions (OR = 31.396; 95% CI: 3.427–287.644; p-value = 0.002), and family member drivers (OR = 15.530; 95% CI: 2.637–91.446, p-value = 0.002) were highly associated with rescheduling the MRI appointments.

Conclusions

We noted higher rates of No-Shows and Rescheduling of MRI appointments in females, those with a lack of formal education, those who had not received the procedure instructions, and those who lacked transportation. We recommend setting targets and developing strategies and policies to improve more timely access to MRI, and thus reduce the waiting time.

Electronic supplementary material

The online version of this article (doi:10.1186/s12913-016-1927-z) contains supplementary material, which is available to authorized users.

From Information Management to Information Visualization: Development of Radiology Dashboards

OBJECTIVE

The development and implementation of a dashboard of medical imaging department (MID) performance indicators.

METHOD

Several articles discussing performance measures of imaging departments were searched for this study. All the related measures were extracted. Then, a panel of imaging experts were asked to rate these measures with an open ended question to seek further potential indicators. A second round was performed to confirm the performance rating. The indicators and their ratings were then reviewed by an executive panel. Based on the final panel's rating, a list of indicators to be used was developed. A team of information technology consultants were asked to determine a set of user interface requirements for the building of the dashboard. In the first round, based on the panel's rating, a list of main features or requirements to be used was determined. Next, Qlikview was utilized to implement the dashboard to visualize a set of selected KPI metrics. Finally, an evaluation of the dashboard was performed.

RESULTS

92 MID indicators were identified. On top of this, 53 main user interface requirements to build of the prototype of dashboard were determined. Then, the project team successfully implemented a prototype of radiology management dashboards into study site. The visual display that was designed was rated highly by users.

CONCLUSION

To develop a dashboard, management of information is essential. It is recommended that a quality map be designed for the MID. It can be used to specify the sequence of activities, their related indicators and required data for calculating these indicators. To achieve both an effective dashboard and a comprehensive view of operations, it is necessary to design a data warehouse for gathering data from a variety of systems. Utilizing interoperability standards for exchanging data among different systems can be also effective in this regard.

Value Innovation in Hospital: Increase Organizational IQ by Managing Intellectual Capitals

Hospital is a complex organization rich in intellectual capitals. Effective management of these assets in line with innovating value to reach strategic goals and objectives can lead to increasing organizational IQ. In hospital with high organizational IQ, Increasing syntropy in intellectual capitals can convert it to an agile, learner, innovative, and smart organization.

An audit of elective outpatient magnetic resonance imaging in a tertiary South African public-sector hospital

Background: Increasing demand for magnetic resonance imaging (MRI) has resulted in longer waiting times for elective MRI, particularly in resource-limited healthcare environments.However, inappropriate imaging requests may also contribute to prolonged MRI waiting times. At the time of the present study, the waiting time for elective MRI studies at Tygerberg Hospital (TBH), a tertiary-level public-sector healthcare facility in Cape Town (South Africa),was 24 weeks.Objectives: To document the nature and clinical appropriateness of scheduled TBH outpatient MRI examinations.Method: A retrospective analysis of the referral forms of all elective outpatient MRIexaminations scheduled at TBH from 01 June to 30 November 2011 was conducted. Patient age, gender, clinical details, provisional diagnosis, examination requested and referring clinician were recorded on a customised data sheet. Two radiologists independently evaluated the appropriateness of each request by comparing the clinical details and the provisional diagnosis provided with the 2012 American College of Radiology (ACR) guidelines for the appropriate use of MRI.Results: Four hundred and sixty-six patients (median age 42 years; interquartile range 19–55) who had 561 examinations were scheduled in the review period; 70 (15%) were children less than 6 years old. Neurosurgery (n = 164; 35%), orthopaedic (n = 144; 31%),neurology (n = 53; 11%) and paediatric (n = 27; 6%) outpatients accounted for the majority(81%) of referrals; 464 (99.6%) were from specialist clinics. MRIs of the spine (n = 314; 56%),brain (n = 152; 27%) and musculoskeletal system (n = 70, 13%) accounted for more than 95%of the investigations. In 455 cases (98%), the referral was congruent with published ACR guidelines for appropriate MRI utilisation.Conclusion: Scheduled outpatient MRI examinations at TBH reflect optimal clinical use of a limited resource. MRI utilisation is largely confined to traditional neuro-imaging. Any initiative to decrease the elective MRI waiting time should focus on service expansion.

The effectiveness of service delivery initiatives at improving patients' waiting times in clinical radiology departments: a systematic review

We reviewed the literature for the impact of service delivery initiatives (SDIs) on patients' waiting times within radiology departments. We searched MEDLINE, EMBASE, CINAHL, INSPEC and The Cochrane Library for relevant articles published between 1995 and February, 2013. The Cochrane EPOC risk of bias tool was used to assess the risk of bias on studies that met specified design criteria. Fifty-seven studies met the inclusion criteria. The types of SDI implemented included extended scope practice (ESP, three studies), quality management (12 studies), productivity-enhancing technologies (PETs, 29 studies), multiple interventions (11 studies), outsourcing and pay-for-performance (one study each). The uncontrolled pre- and post-intervention and the post-intervention designs were used in 54 (95%) of the studies. The reporting quality was poor: many of the studies did not test and/or report the statistical significance of their results. The studies were highly heterogeneous, therefore meta-analysis was inappropriate. The following type of SDIs showed promising results: extended scope practice; quality management methodologies including Six Sigma, Lean methodology, and continuous quality improvement; productivity-enhancing technologies including speech recognition reporting, teleradiology and computerised physician order entry systems. We have suggested improved study design and the mapping of the definitions of patient waiting times in radiology to generic timelines as a starting point for moving towards a situation where it becomes less restrictive to compare and/or pool the results of future studies in a meta-analysis.

The Boston Marathon bombing: after-action review of the Brigham and Women's Hospital emergency radiology response

PURPOSE

To analyze imaging utilization and emergency radiology process turnaround times in response to the April 15, 2013, Boston Marathon bombing in order to identify opportunities for improvement in the Brigham and Women's Hospital (BWH) emergency operations plan.

MATERIALS AND METHODS

Institutional review board approval was obtained with waivers of informed consent. Patient demographics, injuries, and outcomes were gathered, along with measures of emergency department (ED) imaging utilization and turnaround times, which were compared with operations from the preceding year by using the Wilcoxon rank sum test. Multivariate linear regression was used to assess contributors to examination cancellations.

RESULTS

Forty patients presented to BWH after the bombing; 16 were admitted and 24 were discharged home. There were no fatalities. Ten patients required emergent surgery. Blast injury types included 13 (33%) primary, 20 (51%) secondary, three (8%) tertiary, and 19 (49%) quaternary. Thirty-one patients (78%) underwent imaging in the ED; 57 radiographic examinations in 30 patients and 16 computed tomographic (CT) examinations in seven patients. Sixty-two radiographic and 14 CT orders were cancelled. Median time from blast to patient arrival was 97 minutes (interquartile range [IQR], 43-139 minutes), patient arrival to ED examination order, 24 minutes (IQR, 12-50 minutes), order to examination completion, 49 minutes (IQR, 26-70 minutes), and examination completion to available dictated text report, 75 minutes (IQR, 19-147 minutes). Examination completion turnaround times were significantly increased for radiography (52 minutes [IQR, 26-73 minutes] vs annual median, 31 minutes [IQR, 19-48 minutes]; P = .001) and decreased for CT (37 minutes [IQR, 26-50 minutes] vs annual median, 72 minutes [IQR, 40-129 minutes]; P = .001). There were no significant differences in report availability turnaround time (75 minutes [IQR, 19-147 minutes] vs annual median, 74 minutes [IQR, 35-127 minutes]; P = .34).

CONCLUSION

The surge in imaging utilization after the Boston Marathon bombing stressed emergency radiology operations. Process analysis enabled identification of successes and opportunities for improvement in ongoing emergency operations planning. © RSNA, 2014.

Assessing the performance of medical personnel involved in the diagnostic imaging processes in mulago hospital, kampala, Uganda

Objectives:

Uganda, has limited health resources and improving performance of personnel involved in imaging is necessary for efficiency. The objectives of the study were to develop and pilot imaging user performance indices, document non-tangible aspects of performance, and propose ways of improving performance.

Materials and Methods:

This was a cross-sectional survey employing triangulation methodology, conducted in Mulago National Referral Hospital over a period of 3 years from 2005 to 2008. The qualitative study used in-depth interviews, focus group discussions, and self-administered questionnaires, to explore clinicians’ and radiologists’ performancerelated views.

Results:

The study came up with following indices: appropriate service utilization (ASU), appropriateness of clinician's nonimaging decisions (ANID), and clinical utilization of imaging results (CUI). The ASU, ANID, and CUI were: 94%, 80%, and 97%, respectively. The clinician's requisitioning validity was high (positive likelihood ratio of 10.6) contrasting with a poor validity for detecting those patients not needing imaging (negative likelihood ratio of 0.16). Some requisitions were inappropriate and some requisition and reports lacked detail, clarity, and precision.

Conclusion:

Clinicians perform well at imaging requisition-decisions but there are issues in imaging requisitioning and reporting that need to be addressed to improve performance.

Assessing the performance of imaging health systems in five selected hospitals in Uganda

OBJECTIVES

The first objective of the study was to develop an index termed as the 'Imaging Coverage' (IC), for measuring the performance of the imaging health systems. This index together with the Hospital-Based Utilization (HBU) would then be calculated for five Ugandan hospitals. Second, was to relate the financial resources and existing health policy to the performance of the imaging systems.

MATERIALS AND METHODS

This was a cross-sectional survey employing the triangulation methodology, conducted in Mulago National Referral Hospital. The qualitative study used cluster sampling, in-depth interviews, focus group discussions, and self-administered questionnaires to explore the non-measurable aspects of the imaging systems' performances.

RESULTS

The IC developed and tested as an index for the imaging system's performance was 36%. General X-rays had the best IC followed by ultrasound. The Hospital-Based Utilization for the five selected hospitals was 186 per thousand and was the highest for general radiography followed by ultrasound.

CONCLUSION

The IC for the five selected hospitals was 36% and the HBU was 186 per thousand, reflecting low performance levels, largely attributable to inadequate funding. There were shortfalls in imaging requisitions and inefficiencies in the imaging systems, financing, and health policy. Although the proportion of inappropriate imaging was small, reducing this inappropriateness even further would lead to a significant total saving, which could be channeled into investigating more patients. Financial resources stood out as the major limitation in attaining the desired performance and there is a need to increase budget funding so as to improve the performance of the imaging health systems.

Quality initiatives: Quality improvement in radiology: basic principles and tools required to achieve success

All imaging departments are expected to establish and maintain effective quality, safety, and performance improvement programs. Essential components of such programs include adherence to the basic principles of quality management and appropriate utilization of quality tools. The initial step is the gathering of relevant information, followed by the collection and analysis of quality and performance data; analysis and ranking of causes that likely contributed to a process failure, error, or adverse event; and prioritization and local implementation of solutions, with careful monitoring of newly implemented processes and wider dissemination of the tools when a process proves to be successful. Quality improvement requires a careful, dedicated, and continuously planned effort by a number of skilled and committed team members, with the goal being to do the right thing in a timely fashion in every case. This process can be sustained by offering rewards and celebrating successes, with all lessons learned disseminated throughout the department or organization.

Enterprise imaging: planning and business justification

To evaluate financial performance, academic radiology departments most often measure examination volume and general technical and professional expenses. Although these metrics are not standardized, their frequency of use reflects that productivity and financial health are high priorities for academic radiology departments across the United States. In this article, we discuss both of these topics, in the context of projects to expand services, particularly those with an information technology (IT) component. First, we discuss several informatics innovations that increase productivity or expand service. Second, we explain core financial analysis concepts applicable to radiology departments. Third, we discuss the unique challenge of evaluating a potential IT project for an academic radiology department, when intangible benefits are difficult to quantify. Financial models are only one of several components used for guidance in strategic decisions, but are crucial to building a business case that justifies the initial or capital investment as well as startup and ongoing operational expenses.

Process analysis to reduce MRI access time at a German University Hospital

QUALITY PROBLEM OR ISSUE

Long access times for magnetic resonance imaging (MRI) can negatively impact the quality of care provided to patients. We investigated improving access by reducing MRI processing time.

INITIAL ASSESSMENT

Data were collected for scans (n= 360) performed over 3 weeks (April-May 2008) at the University Hospital of Mannheim, Germany. Average access time, excluding emergencies, was 44 (±44) days for outpatients and 3 (±5) days for inpatients. Factors influencing total MRI processing time were identified using multivariate linear regression. In addition to region scanned, the total MRI processing time was significantly related to performing multiple scans (β = 33.57, P< 0.01), using oral contrast media (β = 13.58, P< 0.01), placing an intravenous (IV) catheter (β = 5.00, P= 0.04) and scanning patients ≤8 years old (β = 0.41, P= 0.03). Contrary to prior perceptions, emergency cases (5.6%) and late arrivals (12.8% >5 min late) were less than expected.

CHOICE OF SOLUTION

Increasing scheduling flexibility to address non-modifiable process variation and completing preparatory activities outside the scanner room were identified as process improvement targets.

IMPLEMENTATION

Scheduling was adapted to utilize three expected total MRI processing times and IV placement was moved outside the scanner room.

EVALUATION

Planned hardware and software upgrades were completed concurrent to the process improvements. As a result, it was not possible to accurately measure the effect of implementing the scheduling and preparatory activity changes.

LESSONS LEARNED

Clinical study team members' prior perceptions of workflow obstacles did not match the study findings. Utilizing insiders and outsiders during process analysis may limit bias in identification of process improvement opportunities.

What influences clinician's satisfaction with radiology services?

Aim

PACS and teleradiology systems have led to marked changes in the traditional relationship between referring clinicians and hospital radiology departments. The aim of this study was to assess which factors influence clinicians’ satisfaction with modern radiology services.

Method

An Internet-based survey questionnaire was sent to all referring clinicians within a large hospital network.

Results

Fifty-eight percent of 316 clinicians responded to the survey. Seventy percent felt PACS installation had improved reporting time, and 56% felt it had improved working patterns for medical staff. Approachability of radiologists was the only factor significantly associated with increased satisfaction (p = 8 × 10–8). A number of factors were found to be significantly associated with the perceived value of radiology reports, and these are discussed. An increase in clinicians’ confidence in their own radiological skills was not associated with a decrease in the value they placed on radiology reports.

Conclusion

The only factor significantly associated with improved clinician satisfaction was the availability of an approachable radiology service. Availability of PACS did not appear to undermine the value placed on radiology reports.

Current use of computed tomographic urography: survey of the society of uroradiology

OBJECTIVE

To determine uroradiologists' opinions and practices regarding computed tomographic (CT) urography.

METHODS

A Web-based survey was sent via e-mail to all 259 members of the Society of Uroradiology. Of the 229 successfully delivered e-mails, 90 (39%) members responded.

RESULTS

Of 90 uroradiologists, 87% perform CT urography. Compared with intravenous (IV) urography, 69% of uroradiologists use CT urography more than 75% of the time urinary tract imaging is requested; 27% stated that CT urography has completely replaced IV urography. Most uroradiologists perform CT urography using multidetector-row CT alone (79%) and use a 3-phase technique (52%) using a single injection (76%) of contrast material at 3 mL/s (52%) without a compression device (81%) and with the patient in supine position (80%).

CONCLUSIONS

Most uroradiologists use CT urography in their practice today; some no longer perform IV urography. Variability in multidetector-row CT technique suggests that more research is needed to determine the optimal protocol.

Business intelligence tools for radiology: creating a prototype model using open-source tools

Digital radiology departments could benefit from the ability to integrate and visualize data (e.g. information reflecting complex workflow states) from all of their imaging and information management systems in one composite presentation view. Leveraging data warehousing tools developed in the business world may be one way to achieve this capability. In total, the concept of managing the information available in this data repository is known as Business Intelligence or BI. This paper describes the concepts used in Business Intelligence, their importance to modern Radiology, and the steps used in the creation of a prototype model of a data warehouse for BI using open-source tools.

Survey of the Use of Quality Indicators in Academic Radiology Departments

OBJECTIVE

Our purpose was to determine whether quality in academic radiology departments in the United States is systematically measured through indicators and evaluated by preset standards.

MATERIALS AND METHODS

We performed a cross-sectional study using a validated survey sent to Society of Chairmen of Academic Radiology Departments (SCARD) members and studied type, frequency of monitoring, and use of preset standards for evaluation of quality indicators. Statistical methods were descriptive summary statistics, chi-square test, analysis of variance, and Spearman's rank correlation test.

RESULTS

The response rate was 42% (55/132). Most responding hospitals were from the Northeast (20/55, 36.4%) and Midwest (18/55, 32.7%). About 58% (32/55) of the responding hospitals had more than 500 beds in operation; 50.9% (28/55) of the radiology departments performed 200,000-400,000 examinations per year. Among the 80% of departments (44/55) that monitored patient satisfaction, only 49.1% and 45.5% assessed referring physician and employee satisfaction, respectively. The most frequently monitored customer satisfaction indicator, patient satisfaction, was monitored quarterly or less frequently by 70.5% (31/44) of departments; about 45.5% (20/44) had preset standards for this indicator. MRI and CT were monitored for patient appointment access by 80% (44/55) and 72.7% (40/55) of departments, respectively; 59.1% (26/44) and 62.5% (25/40) of departments applied preset standards to these indicators, respectively. The reporting-time indicator monitored most frequently was report turnaround time (45/55, 81.8%). None of the differences in mean numbers and monitoring frequencies of the indicators and the use of preset standards to evaluate them by region and size of departments were significant (p >0.05).

CONCLUSION

Use of quality management indicators, particularly customer satisfaction indicators, is not a fully standardized and established process for academic radiology departments in the United States.

Trends and future needs in clinical radiology: insights from an academic medical center

OBJECTIVE

Advances in technology, expanding indications and defensive medical practice, in combination with population aging, have all contributed to a substantial increase in utilization of imaging and therapeutic radiology procedures in recent years. Moreover, the integration of education, innovation and research into high-volume workflow, although challenging, is a key requirement in teaching hospitals. Therefore, identifying forthcoming demand in the use of radiology services at a referral center might be of special interest and facilitate health policy planning in this context.

METHODS

Data regarding conventional radiographic, ultrasonographic and computed tomography (CT) investigations, radiotherapy sessions, and interventional procedures were collected for a 5-year period (2000-2004). Based on these observations, we deployed appropriate models to forecast utilization rates in 2005-2009.

RESULTS

Between 2000 and 2004, ultrasound examinations increased by 31.8%, mammography by 31.6%, CT scans by 17.4%, interventions by 14.5% and radiotherapy sessions by 13.9%, while conventional investigations decreased by 42.5%. We identified significant increasing trends for ultrasound, mammography, CT and interventions (all p<0.001 for linear component). Compared to current levels, the workload for these modalities is expected to rise in the next 5 years by 43%, 31%, 20% and 14%, respectively. Radiotherapy sessions demonstrate an unstable, non-significant increasing trend (p=0.189), while utilization of conventional radiography declines rapidly (p<0.001 for linear trend, 5-year prediction -51%).

CONCLUSIONS

In forthcoming years, the demand for radiology services at referral centers will increase substantially. Advances in digital technology alone will not suffice to completely alleviate the need for additional resources and well-trained personnel.

Use of Productivity and Financial Indicators for Monitoring Performance in Academic Radiology Departments: U.S. Nationwide Survey

PURPOSE

To determine how productivity- and finance-related indicators are used by radiology departments to evaluate departmental performance.

MATERIALS AND METHODS

The study met the criteria to be exempt from institutional review board approval. All subjects were informed of the purpose of the study and that their questionnaire responses would be kept confidential. For the study, a survey was sent to 132 members of the Society of Chairmen of Academic Radiology Departments (SCARD) nationwide. The survey was designed to (a) assess organizational information about hospital and radiology departments, (b) determine the types and mean numbers of productivity and financial indicators used by radiology departments, (c) determine how these indicators are used to influence departmental productivity, and (d) assess the reference-standard goals with which each indicator value was compared. A total of 77 variables were studied. Summary statistics, Spearman rank correlation coefficient, and chi2 analyses were performed.

RESULTS

The response rate was 42% (55 of 132 surveyed SCARD members). The mean number of productivity indicators used by radiology departments was 4.55 +/- 2.56 (standard deviation), while the mean number of financial indicators used was 2.89 +/- 1.99. Twenty-two (40%) of the 55 responding departments used productivity indicators to monitor and provide feedback to radiologists, hospital leaders, and technical staff members for improved productivity, but only 11 (20%) departments used these indicators to compare personnel performances against specific productivity standards. The most frequent goal (of seven [13%] responding departments) of using the indicators was to increase the examination volume from the previous year by 5%-10%.

CONCLUSION

Academic radiology departments across the United States do not use a standardized set of productivity and financial indicators to measure departmental performance. Examination volume is the most frequently used productivity indicator, whereas general expenses are commonly used as indicators of financial status.