Driving Question:
How can Clinical Decision Support Systems (CDSS) improve the quality of
healthcare?
Assignment:
CHITS is an electronic medical record currently being used by many regional
health units nationwide. Think of a clinical scenario and suggest a clinical
decision support system embedded within CHITS to address this.
Decision Support System
Decision Support Systems (DSS) are a specific class of
computerized information system that supports business and organizational
decision-making activities. A properly designed Decision Support System is an
interactive software-based system intended to help decision makers compile
useful information from raw data, documents, personal knowledge, and/or
business models to identify and solve problems and make decisions. [1]
From the definition, it is a tool that can be used to
influence decision making and actions on a certain aspects of a problem presented.
DSS was designed to adapt to a rapid changes surrounding a business. The
management which has the bird’s eye view of a business environment uses this
tool for hasty resolution by means of deciphering the information gathered:
- · inventories of information assets (including legacy and relational data sources, cubes, data warehouses, and data marts),
- comparative sales figures between one period and the next,
- · projected revenue figures based on product sales assumptions.
Clinical Decision Support System
A clinical decision support system (CDSS) is an application
that analyzes data to help healthcare providers make clinical decisions. A CDSS
is an adaptation of the decision support system commonly used to support
business management.
Physicians, nurses and other health care professionals use a
CDSS to prepare a diagnosis and to review the diagnosis as a means of improving
the final result. Data mining may be conducted to examine the patient’s medical
history in conjunction with relevant clinical research. Such analysis can help
predict potential events, which can range from drug interactions to disease
symptoms. [3]
This is an excellent tool for healthcare. Basically the
birth of medical informatics ignited the more effective use of Clinical
Decision Support System. Based from the article of www.clinfowiki.org, the CDSS can be traced
way back 1959 when “Ledley and Lusted propose a mathematical model for
diagnosis in their article "Reasoning foundations of medical diagnosis;
symbolic logic, probability, and value theory aid our understanding of how
physicians reason", published in Science.”
The conception of medical informatics imposed the need for
more support to a health worker’s (physician, nurses, etc.) sound clinical judgment.
The data, information and knowledge obtained in a particular case could result
to multiple meanings and outcomes. CDSS whether it is paper based or electronic,
tries to narrow down the options for more timely and sufficient interventions.
Electronic applications such as PHR’s, EMR’s and other data
consolidating medical applications can are good examples of tools that can be
used for data mining and data building. CHITS (Community
Health
Information Tracking System) an EMR (Electronic Medical Record) developed for
community health centers, records patient profiles from womb to tomb.
Basically, it can gather all the needed information of patient’s health domain
from conception to death using the programs inside this application. Upon
patient encounter, the community health workers can immediately identify the
services to be rendered. It can also enable the HC physicians to decide whether
this patient should be referred to a higher level facility by means of
identifying the diagnosis rooting from subjective and objective data. And the
RHU/ HC can consolidate everything from mortality, morbidity, inventory,
maternal count, Philhealth reports, etc. Quoting my previous assignment submitted o Dr.
Mike Muin, “For national
institutions like the Main DOH and CHD’s, this information is vital for
decision support. It can immediately pin point what areas are lacking
particular medications or services through identifying the red areas from the
gathered reports from the primary care level. The decision support feature of
each EMR can also be utilized the primary care units and provincial health
offices. They can be alerted upon seeing the results and they can act on it
immediately by allocating the needed services and medications focusing on a
particular alert area.”
The scenario:
Patient X, male, 47 years old suddenly experienced nape pain,
severe headache and blurring of vision after picking up tomatoes in his farm.
The blurring of vision and nape pain disappeared after trying to rest in a
shade. Her wife was worried after hearing his complaints. Since the nearest
hospital is 3 hours away from his house, the wife brought her husband to the
RHU located in the Poblacion of the municipality. This RHU is using CHITS as
its EMR.
The admitting section added Patient X to the database by
interviewing and encoding real time. She gathered the needed data:
Name
Address
Birthday (automatic computation of age)
Sex/ Gender
Civil Status
She then took the patient’s height (156 cm) and weight (53
kg). CHITS computed his BMI as “Normal” after saving. She also recorded his
vital signs BP: 160/100, PR: 97 bpm, RR: 17, Temp: 36.9. She captured his
complaints, “Nape pain, sudden blurring of vision, nausea and vomiting then
queued him to the physician.
After seeing the complaints, the Physician gathered his
medical and family history. Patient X mentioned that his mother was
hypertensive and has diabetes and was also diagnosed in the center. The
physician confirmed that his mother has the mentioned diseases after opening
her electronic record. He also added that he smokes 10 sticks a day and occasionally
drinks alcohol. The physician examined Patient X’s vision using the Snellen. For
his age, he has a remarkable vision. He then encoded the findings in the
general consultation of CHITS. He gave Patient X a diagnosis of Stage 2
Hypertension after entering it in the EMR, then prescribed 5 mg Metoprolol once
daily as his medication. He advised the patient to come to the RHU daily for at
least 7 days for BP monitoring. Patient X proceeded to the pharmacy to redeem
the free medicines. Unfortunately, the CHITS inventory showed that 50 mg tablets
are out of stock. She then gave 100 mg tablets and instructed him to cut it in
half before taking it. After receiving some advise patient was “end consulted”
and went home.
For the past 7 days, Patient X visits the RHU. The admission
staff records his physiologic data in the EMR. CHITS consolidates all the data every
patient encounter. Day 7 came; the physician noticed that Patient X’s BP
dropped a little from 160/100 to 140/90 with an intermittent trend. He asked
the patient of medication compliance and he affirmed by showing the medication
foils. The physician decided to increase patient’s metoprolol to 100 mg. After
another round of BP charting, Patient X’s hypertension was controlled.
From the scenario, it is clear that the physician relied from
the patient’s monitoring chart on the EMR. It helped him identity whether the
medication given was adequate in controlling Patient X’s blood pressure. He
also based his diagnosis on the series of information derived from the EMR. Aside
from the accurate recording, it can also tally the inventory (medications,
supply, etc.) for better accounting. These are just some example of the
positive outcomes of a CDSS.
References:
[2] “Decision
Support System” http://en.wikipedia.org/wiki/Decision_support_system
[3] “Clinical Decision Support
System” Source:http://searchhealthit.techtarget.com/definition/clinical-decision-support-system-CDSS
No comments:
Post a Comment