This work aimed to apply a BI process in the routine of a large hospital to better manage the consumption of ICA, which is the material with the highest direct expense cost in the CT scan unit. The main interest of managers and business analysts was to search for an opportunity to lower costs, increasing the competitiveness of the organization in the market and generating the greatest possible benefit to the patient.
Survey of the interests of the managers and a definition of business questions about the consumption of ICA in CT scans.
At first, a set of business questions was defined empirically, regarding the evaluation of the ICA volume consumed per scan, and the ratio of volume injected to patient mass. The objective was to identify opportunities to improve assistance processes and reduce expenses of material consumption in the CT scans.
The business questions formulated at first were structured and evaluated through a consultation with the managers of the areas of management, medicine, administration, radiological techniques, and nursing, through a semi-directed interview based on the methodology of Marconi and Lakatos (2010). From this point onwards, we formulated a new business question, more closely related to the interests manifested by the interviewees. As a result, this work was based on the following business question: in which situations is it possible to identify the waste caused by the use of excessive materials?
To better understand the analysis, one must consider:
1) Approach to answer the business question:
a) identifying, in the attention to patients who undergo CT exams with the use of ICA the relations and dependences between (I) the volumes injected and (II) the ratio of volume injected to patient mass, with other variables.
2) Information generated:
a) injected volume, in mL;
b) ratio of injected volume to patient in mL/Kg;
considered by:
· the patient (patient, mass group, age group, age, mass, and sex);
· exam (specialty, part of the body, name of the exam);
· exam request (request, origin of the attention);
· exam room (exam room, equipment);
· radiology technician (name of the radiology technician);
· nursing technician (name of the nursing technician);
· injection (injection flow, type of injection);
· material (concentration, cost, batch);
· adverse event (extravasation, volume of extravasation, reaction);
· date (date, year, semester, trimester, month, day, day of the week;
· time (time, hour, shift).
3) Importance of the information:
a) identifying whether there is a pattern of volume and of volume/mass injected per exam and its dependences, to evidence and investigate situations in which there are signs of material being wasted.
This approach allowed the structuring of data to carry out analysis of the information from different perspectives or dimensions.
Selection of variables of interest from operational source system
To answer the business question, a search was carried out in the Qdoc for a set of variables that could describe attention with the involvement of ICA injections during CT scans in patients. At first, 24 variables correlated to the care and the consumption of ICA were selected. These were already part of the care flow, and their data was filled in in the Qdoc system.
Figures 2 and 3 present the variables of interest of the operacional source system from two perspectives, respectively: form of existence (native or created) and form of input (automatic or manual). Some of these variables are native to the Qdoc system, that is, they exist as a standard inside the software; others were created in the system, by request of the Radiology Service. They were customized to attend to the specific assistance and managerial needs of the institution. Figure 2 shows, in a flowchart, the 24 variables selected from the Qdoc system, highlighting that 15 were native, while 9 were created.
Another criteria to distinguish between the variables is related to the form in which the data is input. It can be automatic, through the integration between the HIS and RIS systems, or manual, through the input of users. As Figure 3 shows, the data input in the Qdoc system is partly automated, through the process of integration with the hospital system. This takes place when the attention of a new patient starts, which means that a set of data previously registered at the HIS gets transfered to the RIS without the need of filling it in again. A set of 12 variables of interest is inputted in this manner. The other 12 variables depend on the manual input of the Nursing team, as part of the care during the performance of the exam.
ETL of the Qdoc for the multidimensional model of the Impax BI DW
After the variables of interest were found in the Qdoc system, a multidimensional model of the Impax BI DW was searched, one that could be adapted and used for the objectives of this work. The Impax BI is a market good developed to attend, globally, to the health organizations in the field of Radiology. The AGFA Healthcare company, a multinational in the RIS and PACS industry, adapts Oracle BI, from the Oracle Corporation, developing some multidimensional models and ETL services in its OLTP (RIS and PACS) services for the DW (Oracle).
The request-procedure model was chosen for use in this work since it has a direct correlation with the care effectively offered, since, in the HCPA flow, the requisition of the exam of the patient is sent to the Qdoc system at the moment the patient arrives for its performance.
The Impax BI allows for adaptations, with the mapping of new variables into the preexisting multidimensional models, a service that the AGFA, its supplier, provides. The client is responsible for requesting new mappings according to the interests of the business.
Therefore, from the 24 variables of interest in the Qdoc, 15 had a native mapping for the Impax BI, while 8 were later mapped by request of the organization. Later, the mapping of the variable “mass” was requested, to a total of 9 variables created and considered in the development of this work. It is important to highlight that the new mappings carried out (9 in total) were made viable because of an agreement with the provider of Impax BI, which included the possible addition of up to 15 variables (5 text, 5 number, and 5 boolean variables).
The process of extraction, transformation, and the loading of data from operational database (Qdoc) to the analytical database (Impax BI) is pre-programmed to take place every 24h, always starting at 12:01 a.m., every day. This inputs all models created with the data generated in the 24h of the previous day.
Adaptation of a multidimensional model in the Impax BI DW and generation of the data cube
The multidimensional request-procedure model, available in the Impax BI DW and associated to the field of attention to patients who undergo Radiology exams was adapted to answer to the business question. The first adaptation was the incorporation of the variable of interest patient mass, created in the Qdoc, in the ETL routine, and its mapping in the multidimensional request-procedure model. Therefore, the values filled in to the Qdoc during the attention of the patient started to be extracted from the operational database and to be loaded into the analytical database. With that, as presented in the 1st Quadrant of Table 1, the 24 variables of interest were complete in the same analytical environment.
Since this is a preexisting model, a set of complementary variables of interest, available for use, was developed, according to what is presented in the 2nd quadrant of Table 1. This set of variables is part of those that already existed in the model and was selected to facilitate the analyses.
The second adaptation necessary was the creation, within the multidimensional request-procedure model, of secondary variables and calculated measures. This set of variables, created within the multidimensional model, was selected for use and adaptation and is in the 3rd quadrant of Table 1. For a better perception of the adaptations carried out in the model during the development of the work, the lines of the table related to them are highlighted in a dark tone.
Table 1 Set of variables used in the multidimensional request-procedure model with a description of the variable and of its type of existence in the model
Set
|
Variable ID
|
Name of the variable
|
Description of the variable
|
Type of existence in the model
|
1st quadrant: Native and created variables of interest generated by the transaction system and imported into the model
|
1
|
Date
|
Date in which the patient was cared for
|
NM
|
2
|
Time
|
Time in which the attention took place
|
NM
|
3
|
Age (years)
|
Age of the patient at the day of the attention
|
NM
|
4
|
Sex
|
Sex of the patient
|
NM
|
5
|
Patient mass
|
Mass of the patient at the day of the attention
|
CP
|
6
|
Name of the exam
|
Name of the exam
|
NM
|
7
|
Part of the body
|
Part of the body analyzed
|
NM
|
8
|
Modality
|
Diagnostic modality (CT scan, magnetic resonance imaging, X-ray, ultrasound scan, etc.)
|
NM
|
9
|
Origin of attention
|
Classification of the origin of the attention (outpatient clinic, hospitalization, emergency, external)
|
NM
|
10
|
Clinical information
|
Information described by the physician requesting the exam, together with the request and including relevant information on the clinical issues of the patient and the reason why the exam was requested. It accompanies a set of answers to questions of interest of the radiologist, which aim to aid in the medical diagnostic.
|
NM
|
11
|
Exam room
|
Room in which the exam was conducted (room 1, room 2, room 3, all rooms)
|
NM
|
12
|
ICA concentration (mg iodine/mL)
|
ICA concentration (300 mg iodine/mL, 350 mg iodine/mL, all concentrations)
|
CO
|
13
|
Batch number
|
Number of the batch of the ICA
|
CO
|
14
|
Form of ICA injection
|
Manner in which the ICA was injected (automated with an injector pump, manual with a syringe, all manners)
|
CO
|
15
|
Injection flow of ICA (mL/s)
|
Injection flow of the ICA through an endovenous pathway.
|
CO
|
16
|
Occurrence of adverse reactions
|
Occurrence of adverse reactions
|
CO
|
17
|
Occurrence of extravasation
|
Occurrence of extravasation
|
CO
|
18
|
Amount of extravasation
|
Amount of extravasation
|
CO
|
19
|
Pacient_code
|
Sequential number of the attention within the RIS system
|
NM
|
20
|
Request_code
|
Sequential number in the HIS system of exam requests
|
NM
|
21
|
Exam_code
|
Identification code of the exam
|
NM
|
22
|
Radiology Technician_code
|
Identification code of the Radiology technician
|
NM
|
23
|
Nursing
Technician_code
|
Identification code of the Nursing technician
|
NM
|
24
|
ICA volume (mL)
|
Records of the volume of ICA injected in the patient
|
CO
|
2nd quadrant: complementary variables of interest that already existed in the model
|
25
|
Year
|
Year in which attention took place
|
EM
|
26
|
Month
|
Name of the month in which attention took place
|
EM
|
27
|
Day
|
Number of the day in which the attention took place
|
EM
|
28
|
Weekday
|
Weekday in which the attention took place
|
EM
|
29
|
Semester
|
Name of the semester in which attention took place
|
EM
|
30
|
Trimester
|
Name of the trimester in which attention took place
|
EM
|
31
|
Hour
|
Hour in which the attention took place
|
EM
|
32
|
Equipment
|
Equipment used to carry out the exam, including a description of the producer and number of channels (Ge 8 channels, Phillips - 16 channels, Toshiba - 64 channels, all)
|
EM
|
33
|
Name of the Radiology technician
|
Name of the Radiology technician that carried out the attention
|
EM
|
34
|
Name of the Nursing technician
|
Name of the Nursing technician that carried out the attention
|
EM
|
35
|
No. of exams
|
Measure - sum of exams
|
EM
|
3rd quadrant: secondary variables and measurements calculated and created in the model
|
36
|
Shift
|
Work shift (morning – 7:00 a.m. to 12:59 p.m, afternoon – 1:00 p.m. to 6:59 p.m, Night – 7:00 p.m to 6:59 a.m., all)
|
CM
|
37
|
Age group
|
Age group of the patient at the day of the attention
|
CM
|
38
|
Mass range
|
Mass range of the patient at the day of the attention
|
CM
|
39
|
Specialty
|
Medical specialty of radiological diagnostic
|
CM
|
40
|
Exam group
|
Group of the exam (tomography or angiotomography)
|
CM
|
41
|
Cost (R$/mL)
|
Ratio of financial value to ICA volume
|
MC
|
42
|
Volume per mass (mL/Kg)
|
Ratio of the volume of ICA injected in the patient to the mass of the patient
|
MC
|
43
|
Reference values for volume (mL)
|
Reference values for the volume of ICA to be injected for each combination of exam and weight range of the patient
|
MC
|
44
|
Excess (mL)
|
Volume of ICA injected above the volume limit
|
MC
|
45
|
Cost of the excess (R$)
|
Cost of the volume of ICA injected above the volume limit
|
MC
|
46
|
Rate of contrasted patients (%)
|
Rate of variable 47 to variable 35
|
MC
|
47
|
No. of contrasted exams
|
Sum of the exams where variable 12 was filled in
|
MC
|
48
|
Maximum volume per exam (mL/exam)
|
Ratio of the sum of the volume of ICA injected in the patient to the sum of the exams carried out
|
MC
|
Captions: Acronyms correspond to the form of existence in the multidimensional request-procedure model: NM - native Qdoc variable that previously existed in the model; CO - variable created in the Qdoc and imported into the model by request of the organization; CP - variable created at the Qdoc and imported to into the model at the request of the researchers; EM - variable that already existed in the model; CM - secondary variable created in the model by the researchers; MC - measure calculated and created in the model by the researchers.
It should be highlighted that the data considered for the development of this work was generated starting in the 1st of July, 2017. The generation of the data cube from the adapted multidimensional request-procedure model was carried out through the application of the following set of filters:
a) period from 07/01/2017 to 06/30/2019;
b) age of the patient equal or above 18 years of age;
c) the modality is CT;
d) ICA concentration: is equal to the concentration of 300 mg iodine/mL or 350 mg iodine/mL, that is, the records indicate the use of ICA in the concentrations of interest;
e) ICA volume: is not null, that is, the volume of ICA used is filled in;
f) ICA volume/mass of the patient: is not null, that is, the ratio of the volume of ICA injected to the mass of the patient is a valid value;
g) ICA injection flow: is not null, that is, the injection flow of the ICA used is filled in;
h) Mass of the patient: is lower than 300kg, that is, extremely high values, possible results of mistyping of the ICA volume, are excluded;
i) Mass of the patient: is higher than 20kg, that is, extremely low values, possible results of mistyping of the ICA volume, are excluded;
The cube formed by the filters applied was made up by 35,388 records.
Construction of an investigative dashboard for the use of OLAP operations
The multidimensional model chosen and the data cube generated lead to a series of viable analyses. Considering the study of situations in which there was waste due to the excessive use of the material, an investigative dashboard was created. It is made up of four charts, making it possible to simultaneously explore all of its variables, forming any desired combination of analysis in real time.
The proposal of the investigative dashboard consolidates four charts that have their presentations altered simultaneously according to the selection, made by the users, of the filters applied to the variables. This structuring is aimed to allow OLAP operations using the data set, enabling slice, dice, drill-dawn, and roll-up procedures.
To facilitate these operations in the OLAP cube, a prompt was created, that is, a system component which is a command area for the selection of data, which was made available in the dashboard, with the charts. The prompt allows the user to perform ad hoc explorations according to their interest, applying the same set of selections to all dashboard charts. Figure 4 presents the final dashboard generated, which brings in its wake an investigative proposal which can be adopted by other CT services.
Consumption and manipulation of data in the investigative dashboard
There was a significant discovery after multiple investigations through ad hoc exploration, starting from the selection of filters in the prompt of the investigative dashboard. This approach considered six perspectives and evaluated varied settings of patient attention. The set of possible combinations explored in this data cube can be noted in Table 2.
Many combinations were tested, always starting from the most general scenario possible and moving towards the most specific one. Figure 5 presents the ratio of the volume to patient mass within a general scenario of the Radiology Service, considering only the year 2018 and the month of June.
Table 2 Analysis of the data cube from six perspectives
Perspectives
|
Selection options
|
When?
|
Year: 2017, 2018, 2019, ou all
Semester: 1st, 2nd, or all
Trimester: 1st, 2nd, 3rd, 4th, or all
Month: free choice of month, or all
Work shift: morning (7:00 a.m. to 12:59 p.m), afternoon (1:00 p.m. to 6:59 p.m), night (7:00 p.m to 6:59 a.m.), or all)
|
Where
|
Room 1, equipped with the 8-channel Ge CT,
Room 2, equipped with the 16-channel Philips CT,
Room 3, equipped with the 64-channel Toshiba CT,
or all rooms
|
Which?
|
Individual choice of the code of the exam,
or all exam codes
|
What?
|
ICA, in a 300 mg Iodine/mL concentration,
ICA, in a 350 mg Iodine/mL concentration,
or all concentrations
|
How?
|
automated ICA injection using an injection pump,
manual ICA injection,
or all injections
|
Who?
|
Individual choice using the code of the radiology technician,
or all radiology technicians
|
The upper-left quadrant of Figure 5 is highlighted, calling attention to the set of undesirable situations in this broad scenario. The definition of undesirable situation was obtained from a semi-directed interview with the managers. Finding the set of variables that contributes for these situations demands many analyses of the data set, carried out from different perspectives.
The result that better explains the undesired situations was obtained through a variation of the CT operator within this set of data. To explain this observation, Figures 6 and 7 show the same set of data, filtered to include only patients attended by Radiology technicians A and B, respectively.
After data exploration, it was found that there is an excellent opportunity for the Service to improve the use of ICA volumes in the attention to patients with mass below 65 kg. This finding became visible in Figure 5, in the highlighted upper-left side of the chart. It could also be noted that the processes of test execution have not been standardized within the Service and with the team, meaning that an effort to this end could make viable a diminution in the consumption of materials, especially for low-mass patients.
It can be noted that the use of simple consultations to databases and reports would not be capable to provide such a fast understanding of the behavior of the data. OLAP technology made it possible to work with many dimensions simultaneously, all of them before analysis. They were considered to be potential contributors for a greater consumption of material. The exploration of data with visual analysis from different perspectives made it possible to identify and prove, through data, whether there was a non-standardized process in the Service, which led to the excessive use of materials.
In this section, there was an attempt to describe the most relevant discovery the investigation made with the data cube to support the decision-making process of the managers.
Planning, actions and controls defined considering evidences and their results
With the aid of the managers, the investigation continued in the database, seeking to identify the starting point that generated the highest financial and assistance impact. The decision about which exam should be conducted first, as a pilot, was based on data, using a strategy that resorted to the Pareto chart to answer the four defined questions together with the managers. The results are presented in the Table 3 below.
Table 3 Support to decision making in the choice of the pilot exam, using the Pareto chart and considering the data cube
Questions of interest of the managers for decision making, about where to start
|
Code of the exam
|
Sum
|
TCAB1
|
TCTX1
|
TCCR1
|
OUTROS
|
1. Among exams that use the ICA, which one happens the most?
|
39%
|
25%
|
7%
|
29%
|
100%
|
2. Among exams that use the ICA in a 300 mg iodine/mL, which one happens the most?
|
47%
|
31%
|
7%
|
15%
|
100%
|
3. Among exams that use the ICA, which one uses the most volume of the material?
|
42%
|
26%
|
5%
|
27%
|
100%
|
4. Among exams that use the ICA, which one uses the most volume of the material in a 300 mg iodine/mL concentration?
|
50%
|
31%
|
5%
|
14%
|
100%
|
TCAB1 (full CT scan of the abdomen), TCTX1 (CT scan of the thorax), and TCCR1 (CT scan of the skull/brain)
It can be noted that the TCAB1 is the most representative, considering the four items analyzed. For that reason, it was chosen to be the pilot in the control of the waste of material. This was a strategic decision of the BI process applied towards a greater impact in clinical and financial results. Considering this data-based decision, the planning, together with the managers, was structured in six groups of activity:
1. Reviewing the protocol of ICA injections in the exam selected to be the pilot;
2. Documenting the information thus generated;
3. Generating weekly automated controls;
4. Guiding the team;
5. Accompanying the data;
6. Assessing the clinical and financial impacts.
Performance of planned activities and results
The actions that put in practice the planning will be presented with the results of each activity and structured following the same order of organization.
Revision of the protocol of ICA injection about the exam chosen to be the pilot
The revision of the protocol of ICA injections for the TCAB1 exam was carried out during multiprofessional weekly meetings involving managers and representatives of the team of Radiology technicians, radiologists, nurses, and physicists. The preexisting guidance available for consultation in the last version of the protocol of exams in the CT unit, revised in January 2019 and referring to the protocol of ICA injections for the TCAB1 exam, was "Intravenous contrast: 1 to 2 mL/kg (up to 150 ml)".
This is an open orientation, leaving the final decision for the Radiology technician at the moment of the execution of the exam. As a result, there was an attempt to find the best association between the volumes injected and the imaging techniques available in the CT Scan, using as a reference the exams from the Service itself, which were evaluated with regard to their quality by the radiologists.
During the review, a strategic change in the protocol was decided. Its aim was to implement guidance with regard to the volume to be used for each concentration and corresponding mass range. Therefore, we tried to achieve a uniform choice of the volume to be injected in the patient and facilitate the decision of the operative team. Finally, a guiding table was formed (Table 4), which is presented below.
Documentation of the information generated
Table 4 shows the new guidance of the Radiology Service for the use of ICA in TCAB1 exams.
Table 4 Protocol of ICA injection for TCAB1 exams containing the volume of reference according to mass range and concentration
Mass range of the patient
|
300 mg iodine/mL
|
350 mg iodine/mL
|
up to 45 kg
|
54 mL
|
45 mL
|
from 46 kg to 55 kg
|
70 mL
|
50 mL
|
from 56 kg to 65 kg
|
80 mL
|
60 mL
|
from 66 kg to 80 kg
|
90 mL
|
75 mL
|
from 81 kg to 95 kg
|
100 mL
|
90 mL
|
more than 96 kg
|
110 mL
|
100 mL
|
Generation of weekly automated controls
After the ICA injection protocol was defined for the exam of TCAB1, two control tools were developed using the interest variables of the multidimensional request-procedure model of Impax BI. The controls were generated in the form of a report and programmed to be automatically sent via email to the interested parties every Monday at 7 a.m. This was done using an agent, a resource available in the Impax BI, which contained all data from the exams from last week, with the period of evaluation lasting from Saturday to Friday.
Team guidance
After the protocol was reviewed, documenting the information and generation of automated controls, the supervisor of radiologic techniques provided guidance to the team of Radiology technicians in a meeting that involved workers from the three work shifts in a uniform manner, in the first week of the second quarter of December 2019. After this date, the operative team is expected to follow the new ICA injection protocol for all TCAB1 exams. The date in which the new protocol starts to be applied is an important milestone for the comparison of the effectiveness of the actions taken, making it possible to compare the scenario there was before the guidance in Table 4 was implemented to the one after its implementation.
Data follow up
Controls started to be sent to the managers and other interested parties who participated in this process in November 9, 2019. The creation of controls before the guidance of the operational team about the new process was important to motivate the team in the search for better results. Furthermore, the period before the guidance of the team about the new process was useful to carry out adjustments in the configuration of reports and data auditing, and from that moment on they started to be delivered with a guarantee that the information contained therein was correct.
After the new ICA injection protocol for TCAB1 exams was in force, there was a diminution in the undesirable situations, above 2 mL/kg, related to the first control. Through the second control, it was possible to verify the standardization of the operational process with a uniformity in the work of the entire team, since the team effectively started to follow the new guidance of ICA injections prescribed by the Service. The strategy of result follow up, with information from the previous week, made it possible for managers to trace actions to intervene, when necessary, in recent occurrences.
Evaluation of clinical and financial impacts
The BI process applied generated clinical and financial impacts that could be perceived and measured. The clinical impacts are shown in Figure 8, where data from the periods A and B are compared, respectively corresponding to the scenarios before and after the ICA injection protocol for TCAB1 exams was changed.
In period B, there was a standardization of the ICA injection volumes according to the mass of the patient, and the attention process for the exam analyzed was more uniform than in period A.
The clinical gains become more perceptible in the analysis of the mean ICA volume injected per exam in a certain monthly period when the sample varies according to the age group of the patient. Comparative charts presented in Figure 9 were generated to evidence a diminution in the mean ICA volume injected in the different mass ranges of the patient. The horizontal line in each of the charts indicates the reference value defined in Table 4. The comparison was obtained through the OLAP slice operation, which was applied to the different mass ranges of patients in the data sample selected for analysis. It was found that the ICA volume diminished in all mass ranges, especially for patients below 65 kg.
A projection was also calculated to predict the financial impact of the actions taken after the implantation of the BI process. It adopted, as a reference, the data cube used in the investigative dashboard. Using it, it was possible to evaluate the economic result generated from the definitions of the ICA injection protocol for the TCAB1 exam, due to its clarity in the guidance and standardization of operational processes. To do so, an analysis was carried out that included two distinct 12 month periods from the data cube, as shown in Table 5.
Table 5 Assessment of the financial impacts for TCAB1 exams
Information about the measurement
|
07/01/2017 to 06/30/2018
|
07/01/2018 to 06/30/2019
|
Annual mean
|
Sum
(24 months)
|
Total volume used
|
719,898 mL
|
705,699 mL
|
712,799 mL
|
1,425,597 mL
|
Volume that could have been used considering the new ICA injection protocol
|
602,638 mL
|
599,850 mL
|
601,244 mL
|
1,202,488 mL
|
Excessive volume used
|
117,260 mL
|
105,849 mL
|
111,555 mL
|
223,109 mL
|
Value of the excess
|
US$ 10,553.40
|
US$ 9,526.41
|
US$ 10,039.95
|
US$ 20,079.81
|
The value used to calculate the projection is based on the last public notice in the electronic bidding model carried out by the HCPA to acquire ICA. The cost of each mL of 300 mg iodine/mL concentration ICA is of US$ 0.09.
The difference between the volume used and the standardized volume was of 15.65%, considering only the TCAB1 exam. Therefore, the projection predicted savings of US$ 10,039.95 per year from 2020 on, in the cost of ICA at the HCPA, considering the mean of the 24 previous months used for this pilot exam. This is the cost of the excessive volume projected to be wasted in one year if the BI project had not been implemented. This result, from 2020 on, contributes directly for the sectoral indicator "revenue commited for consumption", which measures the ratio of total financial revenue of the service to all financial values used to pay materials consumed in the corresponding period.
Assessment of the perception of managers about the process applied
To carry out a quantitative evaluation about the impact and relevance of the BI process applied in the HCPA, a semi-directed interview was elaborated with the management, based on the Marconi and Lakatos (2010) methodology, as a way to measure the results achieved.
The interviews were previously scheduled with the same five managers who participated in the first semi-directed interview at the beginning of the work. The meetings were carried out individually through a web conference, which lasted for a mean of one hour. This period was divided in two stages.
In the first stage, the objectives of the work and of the interview were presented to the managers, as well as the research question and the business question of the investigation, its results and its conclusion. In this stage, a report was carried out about the activities developed in the BI process applied, seeking to elucidate the values reached for the business in clinical and financial terms.
In the second stage of the interview, 10 questions were asked, which had been elaborated by the authors of this work. Their answers were in a scale from 0 to 5, and they aimed to quantify the impact and the relevance of each question, where 0 was low and 5 was high. NA meant "does not apply" that is, the dimension was not within the perspective considered by the interviewee. Finally, a mean percentage of the impact and relevance of each question was found. Results are in the Table 6 below.
Table 6 Impact and relevance in the perception of the managers about the BI process as applied in the HCPA
BI process applied in the HCPA
|
Mean percentage of the impact and relevance in the perception of the managers (%)
|
Was the result presented satisfactory?
|
100%
|
Was it important to identify the waste of ICA?
|
100%
|
Was it important to give support to the decision making process?
|
96%
|
Did it help in the planning of actions and in the definition of controls from the evidence generated?
|
100%
|
Did it help in the control of undesirable situations?
|
100%
|
- ICA injections with values above 2 mL/kg.
|
- Injections above the reference value indicated in the ICA injection protocol for the TCAB1 exam
|
Did it enable a better management of ICA use?
|
100%
|
Did it generate a satisfactory financial result?
|
100%
|
Did it generate benefits for the patients?
|
100%
|
Was it decisive for the follow up of operational routines?
|
100%
|
Should it continue to be used?
|
100%
|
One of the interviewees indicated, in the interview, an additional item, which was classified as having 100% of impact and relevance, which was the extinction of the divergences between the teams of Nursing technicians and Radiology technicians about the volume of ICA that should be injected in the patient. Furthermore, the standardization helped the preparation of the ICA injection pump to be faster, since the volume, now, was predefined.
The general score obtained was of 99.6% of impact and relevance in the perception of the interviewees, with regard to the BI process applied to the HCPA. This value was obtained through a mean between all questions answered by the five interviewees.