The reason why less than half of the applicants came in is because it was already mentioned in the announcement two months ago
Three months before the opening of a branch in Sindosi, a recruitment notice for nursing staff was posted, but less than half of the expected applicants showed up. Upon closer inspection, it turns out that two large hospitals in the vicinity had been recruiting a large number of nurses in the same area and even raised their benefits two months ago. By the time our hospital looked into the local talent pool, the competition had already passed through, and we are now in a position where we have to compete by adding extra money to attract the remaining candidates.
This is a recurring scene in the fiercely competitive medical staffing market. Interestingly, the signal was not hidden anywhere. Both the recruitment notices and the hospital reputations have already been publicly available online. The problem lies in the fact that they are scattered as individual pieces of information based on location, job position, institution, and timing, and they do not reveal anything until they are consolidated into trends.
- Missing the moment when demand concentrates in a specific location or job position compared to competitors
- Finding out about the expansion of competing hospitals through opening news, as they had already been recruiting staff several months ago
- We have never analyzed how our hospital's reputation affects recruitment competitiveness or where we are falling behind compared to competing hospitals using data
Simply skimming through the recruitment notices in the search bar every day does not provide much insight beyond the impression of "seems like more than usual."
Consolidating public recruitment and reputation data into one axis
By utilizing HashScraper, recruitment information and hospital reputation data publicly available online are regularly collected and aggregated based on job position, location, and timing. The goal is not to show individual notices again but to interpret where the demand is concentrated and how competing institutions are moving as trends.
Data Example - Trend of recruitment demand by region and job position (weekly aggregation)
{
"View": "채용 수요 추이 (주간 집계)",
"Region": "○○ 권역",
"Job": "간호사",
"Experience": "3년 이상",
"Weekly Openings": [8, 9, 11, 17, 21, 22],
"Trend": "최근 4주 +140%",
"Collected At": "2026-07-05"
}
Data Example - Hospital reputation (classification of review attributes)
{
"View": "병원 평판",
"Hospital": "○○ 병원 (○○ 권역)",
"Rating": 4.1,
"Review Count": 940,
"Review": {
"Rating": 2,
"Date": "2026-06-18",
"Body": "예약했는데 대기가 길었어요 ...",
"Aspect": ["대기시간"]
},
"Collected At": "2026-07-05"
}
Recruitment information is structured by job position, location, experience, employment type, and recruitment scale into trends, while hospital reputations are categorized by rating, number of reviews, and review content by attributes. When scattered pieces come together on the same axis, the increasing demand, expanding competitors, and crumbling reputations become visible.
Use case: Monitoring the labor market and reputation of a healthcare company
A healthcare company collected and analyzed public recruitment information and hospital reputation data regularly to support medical staff recruitment and branch strategies with data. The labor market varies by region and job position, and hospital reputation not only affects patients but also influences the choices of applicants. However, the signals were scattered, making it challenging to analyze them based on a single criterion.
Collection and analysis settings
- Recruitment trends: Aggregating job position, location, experience, employment type, and recruitment scale on a time basis (trend analysis based on public information, not specific channels)
- Hospital reputation: Location, rating, number of reviews, and review content by hospital
- Analysis: Trend of recruitment demand by region and job position, signals of recruitment and expansion by competing institutions, classification and sentiment analysis of review attributes (response, waiting time, facilities, medical staff, etc.)
- Collection frequency: Regular collection
Quantifiable outcomes
| Item | Description |
|---|---|
| Data scope | Public recruitment trends + hospital reputation |
| Aggregation axis | Job position · Location · Timing |
| Analysis | Trend of demand · Signals of competitive expansion · Classification of review attributes |
| Collection frequency | Regular |
As the scattered signals converged into one axis, questions that were previously left for post-analysis began to find answers through data.
- Surge in demand — By first identifying a turning point such as "the number of nursing job postings in this area has doubled in the past 4 weeks" on a graph, we can post job openings before competitors sweep through the talent pool.
- Basis for benefits — By confirming market practices such as "hiring 3-year experienced nurses in this area explicitly states regular employment conversion and night shift allowances," we establish where our benefits stand compared to the market.
- Expansion signals — When competing institutions start hiring a specific job position several times more than usual, we interpret it as an expansion signal at that time.
Different answers for different roles from the same data
Different departments have different questions about the same integrated data. Previously, these questions were answered based on intuition or post-analysis.
| Role | Question | Answer from data |
|---|---|---|
| Recruitment (HR) | Where is the demand concentrated by region and job position? | Turning points in demand trends by job position and location |
| Workforce planning & compensation | How does our benefits package compare to the market? | Distribution of recruitment conditions by job position and experience level |
| Business development & strategy | Where is the demand high and the workforce active? | Cross-referencing demand indicators with workforce supply indicators |
| Management & market intelligence | Where are competing institutions moving? | Simultaneous signals of increased recruitment and reputation rise |
| Reputation & marketing | Ratings remain the same, so why are we losing applicants? | Trend of negative mentions by review attribute |
| Data & insights | How to turn scattered public data into reports? | Regular aggregation by job position, location, and timing axis |
Especially valuable from a business development perspective is filtering out the 'trap locations.' While statistics on population, aging rate, and number of beds can indicate the size of demand, they remain silent on whether the workforce to fill that demand is actually active in that area.
An area that appears to have high demand may actually be a 'workforce-depleted area' where several institutions have been repeatedly opening the same positions for months. By distinguishing between 'areas where recruitment closes quickly' and 'areas where the same positions are repeatedly opened,' we can avoid the trap of not being able to fully staff half of the hospital wards within 6 months of opening.
Turning ratings and job postings into 'readable forms'
Public data left as is is just a scattered text dump. HashScraper organizes this into analyzable forms using AI.
- Aggregation of recruitment trends: Aggregating recruitment numbers and scale by job position, location, and timing grid to visualize where demand is concentrated and where it is dissipating
- Classification and sentiment analysis of review attributes: Grouping review content by attribute (response time, waiting time, facilities, medical staff, etc.) and determining positive or negative sentiment
- Sample correction comparison: Since the number of reviews varies greatly by hospital (e.g., 120 vs. 900), correcting for sample differences to rank reputations based on the same criteria
As a result, reputations that were previously lumped together based on ratings alone are now read in attribute units like "although the rating remains the same, negative mentions about response have been increasing recently," and demand that was not visible from a single job posting is now revealed as a trend like "demand for this job position is surging in this area."
It can also be used in the following cases
Determining the order of establishment based on a regional talent supply heatmap — By aggregating recruitment demand by job position and location grid, quantitatively setting up combinations with high difficulty in securing talent in advance, designing the order of establishment for new medical departments and branches, and benefits.
Identifying the real cause of reputation deterioration — By overlaying the trend of negative mentions by review attribute and the tense point of regional talent supply, you can distinguish whether the 'sharp increase in waiting time and response complaints' is due to employee training issues or a shortage of staff.
Proactive retention — By pinpointing the start of a surge in demand for a specific job position in the market as an early warning signal of attrition, you can shift from symptom-based retention to cause-based retention.
Identifying the 'offensive phase' of competition — Automatically identifying institutions where recruitment surges (capacity enhancement) and review numbers and ratings increase (demand response) simultaneously as early signals of competitive offense.
Why it's difficult to handle internally
Teams that were trying to handle this data internally faced a common barrier.
First and foremost is the burden of 'consolidating individual pieces into trends.' Even though the information is public, it is scattered based on location, job position, institution, and timing, making it impossible for one person to manually count the 'growth rate' every week by just skimming through.
Moreover, the unique operational burden of web data collection comes into play.
- Maintenance hassle: Public screen structures change frequently, causing the collection process to quietly stop. Dealing with multiple regions and institutions requires constant monitoring.
- Continuous monitoring: If the collection returns 0, it is better to notice it, but if only half of the 'reasonably plausible number' comes in, the gaps are discovered only after the report is distributed. A system for detecting failures and omissions and conducting re-collection is necessary.
- Building and managing collection infrastructure: Regular large-scale collection requires automation, schedulers, and storage to work together, requiring dedicated personnel and costs to set up and maintain.
HashScraper takes on this burden entirely. It collects and aggregates public data regularly, responds to screen changes and collection gaps, and provides the infrastructure, allowing customers to focus solely on interpreting the data without worrying about its integrity.
From raw data to reports and dashboards
The collected and aggregated data can be received as raw data or in the form of reports and dashboards. By displaying trends of demand by job position and location, signals of competitive institutions' recruitment, and hospital reputation benchmarks on one screen, practitioners can read market movements without having to manually search through job postings and reviews. If you have your own analysis system, you can also receive it as is by integrating it with Excel, email, API, or a database.
- Review & VOC Analysis Solution: Review & VOC Data
Summary
Signals in the medical labor market and hospital reputations are already publicly available online. However, scattered individual pieces do not reveal much. By consolidating them based on the axis of job position, location, and timing, we can read trends of surging demand, expanding competitors, and crumbling reputations before others.
HashScraper handles data collection, maintenance, aggregation, and analysis. Once set up, the flow of the labor market and reputation comes in a unified form.
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With HashScraper, you can collect and aggregate public recruitment trends and hospital reputation data based on a single criterion and expand the analysis and visualization.
