Recent　advances　in　Pre-trained　Language　Models　(PrLMs)　have　driven　general　domain　multi-choice　Machine　Reading　Comprehension　(MRC)　to　a　new　level.　However,　they　perform　much　worse　on　domain-specific　MRC　like　biomedicine,　due　to　the　lack　of　effective　matching　networks　to　capture　the　relationships　among　documents,　question　and　candidate　options.　In　this　paper,　we　propose　a　Sketchy-Intensive　(SkeIn)　reading　comprehension　model,　which　simulates　the　cognitive　thinking　process　of　humans　to　solve　the　Chinese　multi-choice　biomedical　MRC　questions:　(1)　obtaining　a　general　impression　of　content　with　a　sketch　reading　process;　(2)　capturing　dedicated　information　and　relationships　of　documents,　question　and　candidate　options　with　an　intensive　reading　process　and　making　the　final　prediction.　Experimental　results　show　that　our　SkeIn　model　achieves　substantial　improvements　over　competing　baseline　PrLMs,　with　average　accuracy　improvements　of　+4.03%　dev/+3.43%　test,　+2.69%　dev/+3.22%　test,　and　5.31%　dev/5.25%　test　from　directly　fine-tuning　BERT-Base,　BERT-wwm-ext　and　RoBERTa-wwm-ext-large,　respectively,　indicating　the　effectiveness　of　SkeIn　to　enhance　the　general　performance　of　PrLMs　on　the　biomedical　MRC　tasks.　©　2021,　Springer　Nature　Switzerland　AG.